diff --git a/.gitignore b/.gitignore index a9c4a2b9..7ac5bd41 100644 --- a/.gitignore +++ b/.gitignore @@ -99,5 +99,11 @@ Icon? .VolumeIcon.icns .com.apple.timemachine.donotpresent +# Python +__pycache__/ +*.pyc + # TypeScript build outputs dist/ +.venv/ +batch-operations/python/psycopg2/.venv diff --git a/java/batch_operations/README.md b/java/batch_operations/README.md new file mode 100644 index 00000000..f5b78caa --- /dev/null +++ b/java/batch_operations/README.md @@ -0,0 +1,114 @@ +# Batch Operations with pgJDBC + +## Overview + +This code example demonstrates how to perform batch DELETE and UPDATE operations in Amazon Aurora DSQL +when working with datasets exceeding the 3,000-row transaction mutation limit. The example uses +[pgJDBC](https://jdbc.postgresql.org/) with the +[Aurora DSQL JDBC Connector](https://github.com/awslabs/aurora-dsql-java-connector) for automatic +IAM authentication. + +Two patterns are provided: + +- **Sequential**: A single-threaded loop that processes rows in configurable-size batches (default 1,000), + committing each batch as a separate transaction. +- **Parallel**: Multiple worker threads each process a disjoint partition of the dataset concurrently using + `hashtext()` partitioning, with each worker running its own batch loop. + +Both patterns include OCC (Optimistic Concurrency Control) retry logic with exponential backoff. + +## About the code example + +Aurora DSQL limits each transaction to 3,000 row mutations. To DELETE or UPDATE more than 3,000 rows, +you must split the work into batches, each committed as a separate transaction. + +The parallel pattern partitions rows across worker threads using +`abs(hashtext(CAST(id AS text))) % num_workers = worker_id`, ensuring workers operate on disjoint sets +of rows and avoid OCC conflicts with each other. + +⚠️ **Important** + +- Running this code might result in charges to your AWS account. +- Each batch is a separate transaction. A failure mid-way leaves the dataset partially modified. + Design your operations to be idempotent where possible. + +## Prerequisites + +- You must have an AWS account, and have your default credentials and AWS Region configured as described + in the [Globally configuring AWS SDKs and tools](https://docs.aws.amazon.com/sdkref/latest/guide/creds-config-files.html) guide. +- Java Development Kit (JDK) 17 or later. +- Gradle (the wrapper is included in this project). +- You must have an Aurora DSQL cluster. For information about creating a cluster, see the + [Getting started with Aurora DSQL](https://docs.aws.amazon.com/aurora-dsql/latest/userguide/getting-started.html) guide. + +## Set up the test table + +Before running the examples, create and populate the test table. Aurora DSQL uses IAM authentication, +so you need to generate a fresh auth token each time you connect with `psql`: + +```bash +export CLUSTER_ENDPOINT="" +export CLUSTER_REGION="" + +# Generate a fresh auth token (expires in 3600 seconds) +export PGPASSWORD=$(aws dsql generate-db-connect-admin-auth-token \ + --hostname $CLUSTER_ENDPOINT \ + --region $CLUSTER_REGION \ + --expires-in 3600) + +psql "host=$CLUSTER_ENDPOINT dbname=postgres user=admin sslmode=verify-full sslrootcert=system" \ + -f batch_test_setup.sql +``` + +## Run the example + +Set environment variables for your cluster: + +```bash +# e.g. "admin" +export CLUSTER_USER="admin" + +# e.g. "foo0bar1baz2quux3quuux4.dsql.us-east-1.on.aws" +export CLUSTER_ENDPOINT="" +``` + +Build and run: + +```bash +./gradlew run --args="--endpoint $CLUSTER_ENDPOINT --user $CLUSTER_USER" +``` + +### Command-line options + +| Option | Default | Description | +|--------|---------|-------------| +| `--endpoint` | (required) | Aurora DSQL cluster endpoint | +| `--user` | `admin` | Database user | +| `--batch-size` | `1000` | Rows per batch transaction (must be < 3000) | +| `--num-workers` | `4` | Number of parallel worker threads | + +## Clean up + +After running the demo, drop the test table to avoid unnecessary storage: + +```bash +# Generate a fresh token if the previous one expired +export PGPASSWORD=$(aws dsql generate-db-connect-admin-auth-token \ + --hostname $CLUSTER_ENDPOINT \ + --region $CLUSTER_REGION \ + --expires-in 3600) + +psql "host=$CLUSTER_ENDPOINT dbname=postgres user=admin sslmode=verify-full sslrootcert=system" \ + -c "DROP TABLE IF EXISTS batch_test;" +``` + +## Additional resources + +- [Amazon Aurora DSQL Documentation](https://docs.aws.amazon.com/aurora-dsql/latest/userguide/) +- [Aurora DSQL JDBC Connector](https://github.com/awslabs/aurora-dsql-java-connector) +- [pgJDBC Documentation](https://jdbc.postgresql.org/documentation/) + +--- + +Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +SPDX-License-Identifier: MIT-0 diff --git a/java/batch_operations/batch_test_setup.sql b/java/batch_operations/batch_test_setup.sql new file mode 100644 index 00000000..3243506d --- /dev/null +++ b/java/batch_operations/batch_test_setup.sql @@ -0,0 +1,214 @@ +-- ============================================================================= +-- Sample table setup for Aurora DSQL batch operations +-- +-- This script creates a test table and populates it with 25,000 rows of sample +-- data distributed across 5 categories (~5,000 rows each). Each category is +-- used by a different demo operation: +-- +-- electronics → Sequential batch DELETE +-- clothing → Sequential batch UPDATE +-- food → Parallel batch DELETE +-- books → Parallel batch UPDATE +-- toys → (unused, remains in table) +-- +-- Aurora DSQL limits each transaction to 3,000 row mutations, so we insert +-- in batches of 1,000 rows. Run each INSERT as a separate transaction. +-- ============================================================================= + +-- Drop the table if it already exists +DROP TABLE IF EXISTS batch_test; + +-- Create the sample table. +-- Uses UUID primary key with gen_random_uuid() to minimize OCC contention, +-- following Aurora DSQL best practice of random keys. +CREATE TABLE batch_test ( + id UUID PRIMARY KEY DEFAULT gen_random_uuid(), + category VARCHAR(50) NOT NULL, + status VARCHAR(20) NOT NULL DEFAULT 'active', + value NUMERIC(10,2), + created_at TIMESTAMPTZ DEFAULT NOW(), + updated_at TIMESTAMPTZ DEFAULT NOW() +); + +-- Create an index on the category column. +CREATE INDEX ASYNC idx_batch_test_category ON batch_test (category); + +-- ============================================================================= +-- Populate the table with 25,000 rows of test data (25 batches of 1,000). +-- Each INSERT is 1,000 rows — run each as a separate transaction. +-- ============================================================================= + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); diff --git a/java/batch_operations/build.gradle b/java/batch_operations/build.gradle new file mode 100644 index 00000000..11e2d424 --- /dev/null +++ b/java/batch_operations/build.gradle @@ -0,0 +1,40 @@ +plugins { + id 'java' + id 'application' +} + +group = 'com.example.dsql' +version = '1.0.0' + +java { + sourceCompatibility = JavaVersion.VERSION_17 + targetCompatibility = JavaVersion.VERSION_17 +} + +repositories { + mavenCentral() +} + +dependencies { + implementation 'org.postgresql:postgresql:42.7.10' + implementation 'com.zaxxer:HikariCP:7.0.2' + implementation 'software.amazon.dsql:aurora-dsql-jdbc-connector:1.4.0' + implementation 'software.amazon.awssdk:dsql:2.42.14' + + testImplementation platform('org.junit:junit-bom:5.11.4') + testImplementation 'org.junit.jupiter:junit-jupiter' + testRuntimeOnly 'org.junit.platform:junit-platform-launcher' +} + +application { + mainClass = 'com.example.dsql.batch_operations.Main' +} + +tasks.named('test') { + useJUnitPlatform() + + testLogging { + events 'passed', 'skipped', 'failed', 'standardOut', 'standardError' + exceptionFormat = 'FULL' + } +} diff --git a/java/batch_operations/gradle/wrapper/gradle-wrapper.jar b/java/batch_operations/gradle/wrapper/gradle-wrapper.jar new file mode 100644 index 00000000..d997cfc6 Binary files /dev/null and b/java/batch_operations/gradle/wrapper/gradle-wrapper.jar differ diff --git a/java/batch_operations/gradle/wrapper/gradle-wrapper.properties b/java/batch_operations/gradle/wrapper/gradle-wrapper.properties new file mode 100644 index 00000000..dbc3ce4a --- /dev/null +++ b/java/batch_operations/gradle/wrapper/gradle-wrapper.properties @@ -0,0 +1,7 @@ +distributionBase=GRADLE_USER_HOME +distributionPath=wrapper/dists +distributionUrl=https\://services.gradle.org/distributions/gradle-9.4.0-bin.zip +networkTimeout=10000 +validateDistributionUrl=true +zipStoreBase=GRADLE_USER_HOME +zipStorePath=wrapper/dists diff --git a/java/batch_operations/gradlew b/java/batch_operations/gradlew new file mode 100755 index 00000000..0262dcbd --- /dev/null +++ b/java/batch_operations/gradlew @@ -0,0 +1,248 @@ +#!/bin/sh + +# +# Copyright © 2015 the original authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 +# + +############################################################################## +# +# Gradle start up script for POSIX generated by Gradle. +# +# Important for running: +# +# (1) You need a POSIX-compliant shell to run this script. If your /bin/sh is +# noncompliant, but you have some other compliant shell such as ksh or +# bash, then to run this script, type that shell name before the whole +# command line, like: +# +# ksh Gradle +# +# Busybox and similar reduced shells will NOT work, because this script +# requires all of these POSIX shell features: +# * functions; +# * expansions «$var», «${var}», «${var:-default}», «${var+SET}», +# «${var#prefix}», «${var%suffix}», and «$( cmd )»; +# * compound commands having a testable exit status, especially «case»; +# * various built-in commands including «command», «set», and «ulimit». +# +# Important for patching: +# +# (2) This script targets any POSIX shell, so it avoids extensions provided +# by Bash, Ksh, etc; in particular arrays are avoided. +# +# The "traditional" practice of packing multiple parameters into a +# space-separated string is a well documented source of bugs and security +# problems, so this is (mostly) avoided, by progressively accumulating +# options in "$@", and eventually passing that to Java. +# +# Where the inherited environment variables (DEFAULT_JVM_OPTS, JAVA_OPTS, +# and GRADLE_OPTS) rely on word-splitting, this is performed explicitly; +# see the in-line comments for details. +# +# There are tweaks for specific operating systems such as AIX, CygWin, +# Darwin, MinGW, and NonStop. +# +# (3) This script is generated from the Groovy template +# https://github.com/gradle/gradle/blob/b631911858264c0b6e4d6603d677ff5218766cee/platforms/jvm/plugins-application/src/main/resources/org/gradle/api/internal/plugins/unixStartScript.txt +# within the Gradle project. +# +# You can find Gradle at https://github.com/gradle/gradle/. +# +############################################################################## + +# Attempt to set APP_HOME + +# Resolve links: $0 may be a link +app_path=$0 + +# Need this for daisy-chained symlinks. +while + APP_HOME=${app_path%"${app_path##*/}"} # leaves a trailing /; empty if no leading path + [ -h "$app_path" ] +do + ls=$( ls -ld "$app_path" ) + link=${ls#*' -> '} + case $link in #( + /*) app_path=$link ;; #( + *) app_path=$APP_HOME$link ;; + esac +done + +# This is normally unused +# shellcheck disable=SC2034 +APP_BASE_NAME=${0##*/} +# Discard cd standard output in case $CDPATH is set (https://github.com/gradle/gradle/issues/25036) +APP_HOME=$( cd -P "${APP_HOME:-./}" > /dev/null && printf '%s\n' "$PWD" ) || exit + +# Use the maximum available, or set MAX_FD != -1 to use that value. +MAX_FD=maximum + +warn () { + echo "$*" +} >&2 + +die () { + echo + echo "$*" + echo + exit 1 +} >&2 + +# OS specific support (must be 'true' or 'false'). +cygwin=false +msys=false +darwin=false +nonstop=false +case "$( uname )" in #( + CYGWIN* ) cygwin=true ;; #( + Darwin* ) darwin=true ;; #( + MSYS* | MINGW* ) msys=true ;; #( + NONSTOP* ) nonstop=true ;; +esac + + + +# Determine the Java command to use to start the JVM. +if [ -n "$JAVA_HOME" ] ; then + if [ -x "$JAVA_HOME/jre/sh/java" ] ; then + # IBM's JDK on AIX uses strange locations for the executables + JAVACMD=$JAVA_HOME/jre/sh/java + else + JAVACMD=$JAVA_HOME/bin/java + fi + if [ ! -x "$JAVACMD" ] ; then + die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME + +Please set the JAVA_HOME variable in your environment to match the +location of your Java installation." + fi +else + JAVACMD=java + if ! command -v java >/dev/null 2>&1 + then + die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH. + +Please set the JAVA_HOME variable in your environment to match the +location of your Java installation." + fi +fi + +# Increase the maximum file descriptors if we can. +if ! "$cygwin" && ! "$darwin" && ! "$nonstop" ; then + case $MAX_FD in #( + max*) + # In POSIX sh, ulimit -H is undefined. That's why the result is checked to see if it worked. + # shellcheck disable=SC2039,SC3045 + MAX_FD=$( ulimit -H -n ) || + warn "Could not query maximum file descriptor limit" + esac + case $MAX_FD in #( + '' | soft) :;; #( + *) + # In POSIX sh, ulimit -n is undefined. That's why the result is checked to see if it worked. + # shellcheck disable=SC2039,SC3045 + ulimit -n "$MAX_FD" || + warn "Could not set maximum file descriptor limit to $MAX_FD" + esac +fi + +# Collect all arguments for the java command, stacking in reverse order: +# * args from the command line +# * the main class name +# * -classpath +# * -D...appname settings +# * --module-path (only if needed) +# * DEFAULT_JVM_OPTS, JAVA_OPTS, and GRADLE_OPTS environment variables. + +# For Cygwin or MSYS, switch paths to Windows format before running java +if "$cygwin" || "$msys" ; then + APP_HOME=$( cygpath --path --mixed "$APP_HOME" ) + + JAVACMD=$( cygpath --unix "$JAVACMD" ) + + # Now convert the arguments - kludge to limit ourselves to /bin/sh + for arg do + if + case $arg in #( + -*) false ;; # don't mess with options #( + /?*) t=${arg#/} t=/${t%%/*} # looks like a POSIX filepath + [ -e "$t" ] ;; #( + *) false ;; + esac + then + arg=$( cygpath --path --ignore --mixed "$arg" ) + fi + # Roll the args list around exactly as many times as the number of + # args, so each arg winds up back in the position where it started, but + # possibly modified. + # + # NB: a `for` loop captures its iteration list before it begins, so + # changing the positional parameters here affects neither the number of + # iterations, nor the values presented in `arg`. + shift # remove old arg + set -- "$@" "$arg" # push replacement arg + done +fi + + +# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script. +DEFAULT_JVM_OPTS='"-Xmx64m" "-Xms64m"' + +# Collect all arguments for the java command: +# * DEFAULT_JVM_OPTS, JAVA_OPTS, and optsEnvironmentVar are not allowed to contain shell fragments, +# and any embedded shellness will be escaped. +# * For example: A user cannot expect ${Hostname} to be expanded, as it is an environment variable and will be +# treated as '${Hostname}' itself on the command line. + +set -- \ + "-Dorg.gradle.appname=$APP_BASE_NAME" \ + -jar "$APP_HOME/gradle/wrapper/gradle-wrapper.jar" \ + "$@" + +# Stop when "xargs" is not available. +if ! command -v xargs >/dev/null 2>&1 +then + die "xargs is not available" +fi + +# Use "xargs" to parse quoted args. +# +# With -n1 it outputs one arg per line, with the quotes and backslashes removed. +# +# In Bash we could simply go: +# +# readarray ARGS < <( xargs -n1 <<<"$var" ) && +# set -- "${ARGS[@]}" "$@" +# +# but POSIX shell has neither arrays nor command substitution, so instead we +# post-process each arg (as a line of input to sed) to backslash-escape any +# character that might be a shell metacharacter, then use eval to reverse +# that process (while maintaining the separation between arguments), and wrap +# the whole thing up as a single "set" statement. +# +# This will of course break if any of these variables contains a newline or +# an unmatched quote. +# + +eval "set -- $( + printf '%s\n' "$DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS" | + xargs -n1 | + sed ' s~[^-[:alnum:]+,./:=@_]~\\&~g; ' | + tr '\n' ' ' + )" '"$@"' + +exec "$JAVACMD" "$@" diff --git a/java/batch_operations/gradlew.bat b/java/batch_operations/gradlew.bat new file mode 100644 index 00000000..e509b2dd --- /dev/null +++ b/java/batch_operations/gradlew.bat @@ -0,0 +1,93 @@ +@rem +@rem Copyright 2015 the original author or authors. +@rem +@rem Licensed under the Apache License, Version 2.0 (the "License"); +@rem you may not use this file except in compliance with the License. +@rem You may obtain a copy of the License at +@rem +@rem https://www.apache.org/licenses/LICENSE-2.0 +@rem +@rem Unless required by applicable law or agreed to in writing, software +@rem distributed under the License is distributed on an "AS IS" BASIS, +@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +@rem See the License for the specific language governing permissions and +@rem limitations under the License. +@rem +@rem SPDX-License-Identifier: Apache-2.0 +@rem + +@if "%DEBUG%"=="" @echo off +@rem ########################################################################## +@rem +@rem Gradle startup script for Windows +@rem +@rem ########################################################################## + +@rem Set local scope for the variables with windows NT shell +if "%OS%"=="Windows_NT" setlocal + +set DIRNAME=%~dp0 +if "%DIRNAME%"=="" set DIRNAME=. +@rem This is normally unused +set APP_BASE_NAME=%~n0 +set APP_HOME=%DIRNAME% + +@rem Resolve any "." and ".." in APP_HOME to make it shorter. +for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi + +@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script. +set DEFAULT_JVM_OPTS="-Xmx64m" "-Xms64m" + +@rem Find java.exe +if defined JAVA_HOME goto findJavaFromJavaHome + +set JAVA_EXE=java.exe +%JAVA_EXE% -version >NUL 2>&1 +if %ERRORLEVEL% equ 0 goto execute + +echo. 1>&2 +echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH. 1>&2 +echo. 1>&2 +echo Please set the JAVA_HOME variable in your environment to match the 1>&2 +echo location of your Java installation. 1>&2 + +goto fail + +:findJavaFromJavaHome +set JAVA_HOME=%JAVA_HOME:"=% +set JAVA_EXE=%JAVA_HOME%/bin/java.exe + +if exist "%JAVA_EXE%" goto execute + +echo. 1>&2 +echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME% 1>&2 +echo. 1>&2 +echo Please set the JAVA_HOME variable in your environment to match the 1>&2 +echo location of your Java installation. 1>&2 + +goto fail + +:execute +@rem Setup the command line + + + +@rem Execute Gradle +"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -jar "%APP_HOME%\gradle\wrapper\gradle-wrapper.jar" %* + +:end +@rem End local scope for the variables with windows NT shell +if %ERRORLEVEL% equ 0 goto mainEnd + +:fail +rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of +rem the _cmd.exe /c_ return code! +set EXIT_CODE=%ERRORLEVEL% +if %EXIT_CODE% equ 0 set EXIT_CODE=1 +if not ""=="%GRADLE_EXIT_CONSOLE%" exit %EXIT_CODE% +exit /b %EXIT_CODE% + +:mainEnd +if "%OS%"=="Windows_NT" endlocal + +:omega diff --git a/java/batch_operations/settings.gradle b/java/batch_operations/settings.gradle new file mode 100644 index 00000000..23e31e80 --- /dev/null +++ b/java/batch_operations/settings.gradle @@ -0,0 +1 @@ +rootProject.name = 'dsql-batch-operations' diff --git a/java/batch_operations/src/main/java/com/example/dsql/batch_operations/BatchDelete.java b/java/batch_operations/src/main/java/com/example/dsql/batch_operations/BatchDelete.java new file mode 100644 index 00000000..a457d732 --- /dev/null +++ b/java/batch_operations/src/main/java/com/example/dsql/batch_operations/BatchDelete.java @@ -0,0 +1,159 @@ +// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +// SPDX-License-Identifier: MIT-0 + +package com.example.dsql.batch_operations; + +import java.sql.Connection; +import java.sql.SQLException; +import java.sql.Statement; +import java.util.ArrayList; +import java.util.List; +import java.util.concurrent.ExecutionException; +import java.util.concurrent.ExecutorService; +import java.util.concurrent.Executors; +import java.util.concurrent.Future; +import javax.sql.DataSource; + +/** + * Sequential and parallel batch DELETE for Aurora DSQL. + */ +public class BatchDelete { + + /** Stop retrying a batch after this many consecutive OCC exhaustions. */ + private static final int MAX_CONSECUTIVE_FAILURES = 10; + + public static int batchDelete( + DataSource pool, String table, String condition, + int batchSize, int maxRetries, long baseDelayMs) + throws SQLException, OccRetry.MaxRetriesExceededException { + + int totalDeleted = 0; + int consecutiveFailures = 0; + while (true) { + try (Connection conn = pool.getConnection()) { + conn.setAutoCommit(false); + String sql = "DELETE FROM " + table + " WHERE id IN (SELECT id FROM " + table + + " WHERE " + condition + " LIMIT " + batchSize + ")"; + + int deleted = OccRetry.executeWithRetry(conn, (c) -> { + try (Statement stmt = c.createStatement()) { + return stmt.executeUpdate(sql); + } + }, maxRetries, baseDelayMs); + + conn.commit(); + totalDeleted += deleted; + consecutiveFailures = 0; + System.out.println("Deleted " + deleted + " rows (total: " + totalDeleted + ")"); + if (deleted == 0) break; + } catch (OccRetry.MaxRetriesExceededException e) { + consecutiveFailures++; + System.out.println("Batch OCC retries exhausted (" + consecutiveFailures + + "/" + MAX_CONSECUTIVE_FAILURES + "), retrying batch with fresh connection"); + if (consecutiveFailures >= MAX_CONSECUTIVE_FAILURES) throw e; + } + } + + // Post-verification: ensure no matching rows remain + try (Connection conn = pool.getConnection(); + Statement stmt = conn.createStatement(); + var rs = stmt.executeQuery("SELECT COUNT(*) FROM " + table + " WHERE " + condition)) { + if (rs.next()) { + int remaining = rs.getInt(1); + if (remaining > 0) { + System.out.println("WARNING: " + remaining + " rows still match condition after deleting " + totalDeleted + " rows"); + } + } + } + + return totalDeleted; + } + + public static int batchDelete(DataSource pool, String table, String condition) + throws SQLException, OccRetry.MaxRetriesExceededException { + return batchDelete(pool, table, condition, 1000, 3, 100); + } + + public static int parallelBatchDelete( + DataSource pool, String table, String condition, + int numWorkers, int batchSize, int maxRetries, long baseDelayMs) + throws SQLException, OccRetry.MaxRetriesExceededException { + + ExecutorService executor = Executors.newFixedThreadPool(numWorkers); + List> futures = new ArrayList<>(); + + for (int i = 0; i < numWorkers; i++) { + final int workerId = i; + futures.add(executor.submit(() -> { + int totalDeleted = 0; + int consecutiveFailures = 0; + String partitionCondition = condition + + " AND abs(hashtext(CAST(id AS text))) % " + numWorkers + " = " + workerId; + + while (true) { + try (Connection conn = pool.getConnection()) { + conn.setAutoCommit(false); + String sql = "DELETE FROM " + table + " WHERE id IN (SELECT id FROM " + table + + " WHERE " + partitionCondition + " LIMIT " + batchSize + ")"; + + int deleted = OccRetry.executeWithRetry(conn, (c) -> { + try (Statement stmt = c.createStatement()) { + return stmt.executeUpdate(sql); + } + }, maxRetries, baseDelayMs); + + conn.commit(); + totalDeleted += deleted; + consecutiveFailures = 0; + System.out.println("Worker " + workerId + ": Deleted " + deleted + " rows (total: " + totalDeleted + ")"); + if (deleted == 0) break; + } catch (OccRetry.MaxRetriesExceededException e) { + consecutiveFailures++; + System.out.println("Worker " + workerId + ": Batch OCC retries exhausted (" + + consecutiveFailures + "/" + MAX_CONSECUTIVE_FAILURES + + "), retrying batch with fresh connection"); + if (consecutiveFailures >= MAX_CONSECUTIVE_FAILURES) throw e; + } + } + return totalDeleted; + })); + } + + executor.shutdown(); + int total = 0; + for (Future future : futures) { + try { + total += future.get(); + } catch (ExecutionException e) { + Throwable cause = e.getCause(); + if (cause instanceof SQLException) throw (SQLException) cause; + if (cause instanceof OccRetry.MaxRetriesExceededException) throw (OccRetry.MaxRetriesExceededException) cause; + throw new SQLException("Worker thread failed", cause); + } catch (InterruptedException e) { + Thread.currentThread().interrupt(); + throw new SQLException("Parallel delete interrupted", e); + } + } + + System.out.println("Parallel delete complete: " + total + " rows deleted by " + numWorkers + " workers"); + + // Post-verification: ensure no matching rows remain (uses original condition, not partitioned) + try (Connection conn = pool.getConnection(); + Statement stmt = conn.createStatement(); + var rs = stmt.executeQuery("SELECT COUNT(*) FROM " + table + " WHERE " + condition)) { + if (rs.next()) { + int remaining = rs.getInt(1); + if (remaining > 0) { + System.out.println("WARNING: " + remaining + " rows still match condition after deleting " + total + " rows"); + } + } + } + + return total; + } + + public static int parallelBatchDelete(DataSource pool, String table, String condition) + throws SQLException, OccRetry.MaxRetriesExceededException { + return parallelBatchDelete(pool, table, condition, 4, 1000, 3, 100); + } +} diff --git a/java/batch_operations/src/main/java/com/example/dsql/batch_operations/BatchUpdate.java b/java/batch_operations/src/main/java/com/example/dsql/batch_operations/BatchUpdate.java new file mode 100644 index 00000000..1d7bf365 --- /dev/null +++ b/java/batch_operations/src/main/java/com/example/dsql/batch_operations/BatchUpdate.java @@ -0,0 +1,161 @@ +// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +// SPDX-License-Identifier: MIT-0 + +package com.example.dsql.batch_operations; + +import java.sql.Connection; +import java.sql.SQLException; +import java.sql.Statement; +import java.util.ArrayList; +import java.util.List; +import java.util.concurrent.ExecutionException; +import java.util.concurrent.ExecutorService; +import java.util.concurrent.Executors; +import java.util.concurrent.Future; +import javax.sql.DataSource; + +/** + * Sequential and parallel batch UPDATE for Aurora DSQL. + */ +public class BatchUpdate { + + /** Stop retrying a batch after this many consecutive OCC exhaustions. */ + private static final int MAX_CONSECUTIVE_FAILURES = 10; + + public static int batchUpdate( + DataSource pool, String table, String setClause, String condition, + int batchSize, int maxRetries, long baseDelayMs) + throws SQLException, OccRetry.MaxRetriesExceededException { + + int totalUpdated = 0; + int consecutiveFailures = 0; + while (true) { + try (Connection conn = pool.getConnection()) { + conn.setAutoCommit(false); + String sql = "UPDATE " + table + " SET " + setClause + ", updated_at = NOW()" + + " WHERE id IN (SELECT id FROM " + table + + " WHERE " + condition + " LIMIT " + batchSize + ")"; + + int updated = OccRetry.executeWithRetry(conn, (c) -> { + try (Statement stmt = c.createStatement()) { + return stmt.executeUpdate(sql); + } + }, maxRetries, baseDelayMs); + + conn.commit(); + totalUpdated += updated; + consecutiveFailures = 0; + System.out.println("Updated " + updated + " rows (total: " + totalUpdated + ")"); + if (updated == 0) break; + } catch (OccRetry.MaxRetriesExceededException e) { + consecutiveFailures++; + System.out.println("Batch OCC retries exhausted (" + consecutiveFailures + + "/" + MAX_CONSECUTIVE_FAILURES + "), retrying batch with fresh connection"); + if (consecutiveFailures >= MAX_CONSECUTIVE_FAILURES) throw e; + } + } + + // Post-verification: ensure no matching rows remain + try (Connection conn = pool.getConnection(); + Statement stmt = conn.createStatement(); + var rs = stmt.executeQuery("SELECT COUNT(*) FROM " + table + " WHERE " + condition)) { + if (rs.next()) { + int remaining = rs.getInt(1); + if (remaining > 0) { + System.out.println("WARNING: " + remaining + " rows still match condition after updating " + totalUpdated + " rows"); + } + } + } + + return totalUpdated; + } + + public static int batchUpdate(DataSource pool, String table, String setClause, String condition) + throws SQLException, OccRetry.MaxRetriesExceededException { + return batchUpdate(pool, table, setClause, condition, 1000, 3, 100); + } + + public static int parallelBatchUpdate( + DataSource pool, String table, String setClause, String condition, + int numWorkers, int batchSize, int maxRetries, long baseDelayMs) + throws SQLException, OccRetry.MaxRetriesExceededException { + + ExecutorService executor = Executors.newFixedThreadPool(numWorkers); + List> futures = new ArrayList<>(); + + for (int i = 0; i < numWorkers; i++) { + final int workerId = i; + futures.add(executor.submit(() -> { + int totalUpdated = 0; + int consecutiveFailures = 0; + String partitionCondition = condition + + " AND abs(hashtext(CAST(id AS text))) % " + numWorkers + " = " + workerId; + + while (true) { + try (Connection conn = pool.getConnection()) { + conn.setAutoCommit(false); + String sql = "UPDATE " + table + " SET " + setClause + ", updated_at = NOW()" + + " WHERE id IN (SELECT id FROM " + table + + " WHERE " + partitionCondition + " LIMIT " + batchSize + ")"; + + int updated = OccRetry.executeWithRetry(conn, (c) -> { + try (Statement stmt = c.createStatement()) { + return stmt.executeUpdate(sql); + } + }, maxRetries, baseDelayMs); + + conn.commit(); + totalUpdated += updated; + consecutiveFailures = 0; + System.out.println("Worker " + workerId + ": Updated " + updated + " rows (total: " + totalUpdated + ")"); + if (updated == 0) break; + } catch (OccRetry.MaxRetriesExceededException e) { + consecutiveFailures++; + System.out.println("Worker " + workerId + ": Batch OCC retries exhausted (" + + consecutiveFailures + "/" + MAX_CONSECUTIVE_FAILURES + + "), retrying batch with fresh connection"); + if (consecutiveFailures >= MAX_CONSECUTIVE_FAILURES) throw e; + } + } + return totalUpdated; + })); + } + + executor.shutdown(); + int total = 0; + for (Future future : futures) { + try { + total += future.get(); + } catch (ExecutionException e) { + Throwable cause = e.getCause(); + if (cause instanceof SQLException) throw (SQLException) cause; + if (cause instanceof OccRetry.MaxRetriesExceededException) throw (OccRetry.MaxRetriesExceededException) cause; + throw new SQLException("Worker thread failed", cause); + } catch (InterruptedException e) { + Thread.currentThread().interrupt(); + throw new SQLException("Parallel update interrupted", e); + } + } + + System.out.println("Parallel update complete: " + total + " rows updated by " + numWorkers + " workers"); + + // Post-verification: ensure no matching rows remain (uses original condition, not partitioned) + try (Connection conn = pool.getConnection(); + Statement stmt = conn.createStatement(); + var rs = stmt.executeQuery("SELECT COUNT(*) FROM " + table + " WHERE " + condition)) { + if (rs.next()) { + int remaining = rs.getInt(1); + if (remaining > 0) { + System.out.println("WARNING: " + remaining + " rows still match condition after updating " + total + " rows"); + } + } + } + + return total; + } + + public static int parallelBatchUpdate(DataSource pool, String table, String setClause, String condition) + throws SQLException, OccRetry.MaxRetriesExceededException { + return parallelBatchUpdate(pool, table, setClause, condition, 4, 1000, 3, 100); + } +} diff --git a/java/batch_operations/src/main/java/com/example/dsql/batch_operations/Main.java b/java/batch_operations/src/main/java/com/example/dsql/batch_operations/Main.java new file mode 100644 index 00000000..a5f8eb23 --- /dev/null +++ b/java/batch_operations/src/main/java/com/example/dsql/batch_operations/Main.java @@ -0,0 +1,129 @@ +// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +// SPDX-License-Identifier: MIT-0 + +package com.example.dsql.batch_operations; +import com.zaxxer.hikari.HikariConfig; +import com.zaxxer.hikari.HikariDataSource; + +import java.sql.SQLException; +import java.util.HashMap; +import java.util.Map; +import javax.sql.DataSource; + +/** + * Main entry point for Aurora DSQL batch operations demo (Java). + * + * Uses HikariCP with the Aurora DSQL JDBC Connector for automatic + * IAM authentication and connection pooling. + * + * Usage: + * gradle run --args="--endpoint <cluster-endpoint> [--user admin] + * [--batch-size 1000] [--num-workers 4]" + */ +public class Main { + + @FunctionalInterface + interface BatchOperation { + int run() throws SQLException, OccRetry.MaxRetriesExceededException; + } + + private static int runOperation(String label, BatchOperation op) + throws SQLException, OccRetry.MaxRetriesExceededException { + System.out.println(); + System.out.println("=".repeat(60)); + System.out.println(" " + label); + System.out.println("=".repeat(60)); + long start = System.currentTimeMillis(); + int total = op.run(); + double elapsed = (System.currentTimeMillis() - start) / 1000.0; + System.out.printf("%n Summary: %d rows affected in %.2fs%n", total, elapsed); + System.out.println("=".repeat(60)); + return total; + } + + /** + * Create a HikariCP DataSource configured for Aurora DSQL with automatic + * IAM authentication via the Aurora DSQL JDBC Connector. + */ + private static HikariDataSource createDataSource(String endpoint, String user, int numWorkers) { + String jdbcUrl = "jdbc:aws-dsql:postgresql://" + endpoint; + + HikariConfig config = new HikariConfig(); + config.setJdbcUrl(jdbcUrl); + config.setUsername(user); + + // Pool sized for parallel workers + config.setPoolName("BatchOpsPool"); + config.setMaximumPoolSize(numWorkers); + config.setMinimumIdle(1); + config.setConnectionTimeout(30000); + config.setMaxLifetime(3300000); // 55 min (connector handles token refresh) + config.setConnectionTestQuery("SELECT 1"); + config.setAutoCommit(true); + + return new HikariDataSource(config); + } + + private static Map parseArgs(String[] args) { + Map config = new HashMap<>(); + config.put("user", "admin"); + config.put("batch-size", "1000"); + config.put("num-workers", "4"); + + for (int i = 0; i < args.length; i++) { + switch (args[i]) { + case "--endpoint": config.put("endpoint", args[++i]); break; + case "--user": config.put("user", args[++i]); break; + case "--batch-size": config.put("batch-size", args[++i]); break; + case "--num-workers": config.put("num-workers", args[++i]); break; + default: + System.err.println("Unknown argument: " + args[i]); + System.exit(1); + } + } + if (!config.containsKey("endpoint")) { + System.err.println("Usage: gradle run --args=\"--endpoint " + + "[--user admin] [--batch-size 1000] [--num-workers 4]\""); + System.exit(1); + } + return config; + } + + public static void main(String[] args) { + Map config = parseArgs(args); + String endpoint = config.get("endpoint"); + String user = config.get("user"); + int batchSize = Integer.parseInt(config.get("batch-size")); + int numWorkers = Integer.parseInt(config.get("num-workers")); + + HikariDataSource pool = createDataSource(endpoint, user, numWorkers); + String table = "batch_test"; + + try { + runOperation("Sequential Batch DELETE (category = 'electronics')", + () -> BatchDelete.batchDelete(pool, table, "category = 'electronics'", batchSize, 3, 100)); + + runOperation("Sequential Batch UPDATE (clothing -> processed)", + () -> BatchUpdate.batchUpdate(pool, table, "status = 'processed'", + "category = 'clothing' AND status != 'processed'", batchSize, 3, 100)); + + runOperation("Parallel Batch DELETE (category = 'food') [" + numWorkers + " workers]", + () -> BatchDelete.parallelBatchDelete(pool, table, "category = 'food'", numWorkers, batchSize, 3, 100)); + + runOperation("Parallel Batch UPDATE (books -> archived) [" + numWorkers + " workers]", + () -> BatchUpdate.parallelBatchUpdate(pool, table, "status = 'archived'", + "category = 'books' AND status != 'archived'", numWorkers, batchSize, 3, 100)); + + } catch (SQLException e) { + System.err.println("Database error: " + e.getMessage()); + e.printStackTrace(); + System.exit(1); + } catch (OccRetry.MaxRetriesExceededException e) { + System.err.println("Max retries exceeded: " + e.getMessage()); + System.exit(1); + } finally { + pool.close(); + } + System.out.println("\nDemo complete."); + } +} diff --git a/java/batch_operations/src/main/java/com/example/dsql/batch_operations/OccRetry.java b/java/batch_operations/src/main/java/com/example/dsql/batch_operations/OccRetry.java new file mode 100644 index 00000000..efffa9a1 --- /dev/null +++ b/java/batch_operations/src/main/java/com/example/dsql/batch_operations/OccRetry.java @@ -0,0 +1,83 @@ +// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +// SPDX-License-Identifier: MIT-0 + +package com.example.dsql.batch_operations; + +import java.sql.Connection; +import java.sql.SQLException; +import java.util.logging.Logger; + +/** + * OCC retry logic with exponential backoff for Aurora DSQL batch operations. + */ +public class OccRetry { + + private static final Logger logger = Logger.getLogger(OccRetry.class.getName()); + private static final String SERIALIZATION_FAILURE = "40001"; + + @FunctionalInterface + public interface DatabaseOperation { + T execute(Connection connection) throws SQLException; + } + + public static class MaxRetriesExceededException extends Exception { + private final int maxRetries; + + public MaxRetriesExceededException(int maxRetries) { + super("Max retries exceeded: failed after " + maxRetries + " retries"); + this.maxRetries = maxRetries; + } + + public int getMaxRetries() { + return maxRetries; + } + } + + /** + * Execute a database operation with OCC conflict retry and exponential backoff. + * + * @param connection a JDBC connection (autoCommit should be false) + * @param operation the database operation to execute + * @param maxRetries maximum retry attempts (default 3) + * @param baseDelayMs base delay in milliseconds for backoff (default 100) + * @return the return value of the operation + */ + public static T executeWithRetry( + Connection connection, + DatabaseOperation operation, + int maxRetries, + long baseDelayMs) throws MaxRetriesExceededException, SQLException { + + for (int attempt = 0; attempt <= maxRetries; attempt++) { + try { + return operation.execute(connection); + } catch (SQLException e) { + if (SERIALIZATION_FAILURE.equals(e.getSQLState())) { + connection.rollback(); + if (attempt >= maxRetries) { + throw new MaxRetriesExceededException(maxRetries); + } + long delayMs = baseDelayMs * (1L << attempt); + logger.warning(String.format( + "OCC conflict, retry %d/%d after %dms", + attempt + 1, maxRetries, delayMs)); + try { + Thread.sleep(delayMs); + } catch (InterruptedException ie) { + Thread.currentThread().interrupt(); + throw new SQLException("Retry interrupted", ie); + } + } else { + throw e; + } + } + } + throw new MaxRetriesExceededException(maxRetries); + } + + public static T executeWithRetry( + Connection connection, + DatabaseOperation operation) throws MaxRetriesExceededException, SQLException { + return executeWithRetry(connection, operation, 3, 100); + } +} diff --git a/java/batch_operations/src/test/java/com/example/dsql/batch_operations/MainTest.java b/java/batch_operations/src/test/java/com/example/dsql/batch_operations/MainTest.java new file mode 100644 index 00000000..d93b25e6 --- /dev/null +++ b/java/batch_operations/src/test/java/com/example/dsql/batch_operations/MainTest.java @@ -0,0 +1,58 @@ +// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +// SPDX-License-Identifier: MIT-0 + +package com.example.dsql.batch_operations; + +import static org.junit.jupiter.api.Assertions.*; + +import org.junit.jupiter.api.BeforeAll; +import org.junit.jupiter.api.Test; + +import java.util.Map; +import java.util.regex.Matcher; +import java.util.regex.Pattern; + +public class MainTest { + + private static String extractRegion(String endpoint) { + Matcher m = Pattern.compile("\\.dsql\\.([^.]+)\\.on\\.aws").matcher(endpoint); + assertTrue(m.find(), "Cannot extract region from endpoint: " + endpoint); + return m.group(1); + } + + @BeforeAll + static void seedDatabase() throws Exception { + String endpoint = System.getenv("CLUSTER_ENDPOINT"); + assertNotNull(endpoint, "CLUSTER_ENDPOINT environment variable is required"); + String user = System.getenv().getOrDefault("CLUSTER_USER", "admin"); + String region = extractRegion(endpoint); + + // Generate auth token + Process tokenProc = new ProcessBuilder( + "aws", "dsql", "generate-db-connect-admin-auth-token", + "--hostname", endpoint, "--region", region, "--expires-in", "3600" + ).redirectErrorStream(true).start(); + String token = new String(tokenProc.getInputStream().readAllBytes()).trim(); + assertEquals(0, tokenProc.waitFor(), "Failed to generate auth token"); + + // Run setup SQL via psql + ProcessBuilder pb = new ProcessBuilder( + "psql", + "host=" + endpoint + " dbname=postgres user=" + user + " sslmode=verify-full sslrootcert=system connect_timeout=10", + "-f", "batch_test_setup.sql" + ); + Map env = pb.environment(); + env.put("PGPASSWORD", token); + pb.inheritIO(); + Process psql = pb.start(); + assertEquals(0, psql.waitFor(), "psql batch_test_setup.sql failed"); + } + + @Test + public void testBatchOperations() { + assertAll(() -> Main.main(new String[]{ + "--endpoint", System.getenv("CLUSTER_ENDPOINT"), + "--user", System.getenv().getOrDefault("CLUSTER_USER", "admin") + })); + } +} diff --git a/javascript/batch_operations/.gitignore b/javascript/batch_operations/.gitignore new file mode 100644 index 00000000..114d0b6d --- /dev/null +++ b/javascript/batch_operations/.gitignore @@ -0,0 +1,3 @@ +/node_modules +/build +/dist diff --git a/javascript/batch_operations/README.md b/javascript/batch_operations/README.md new file mode 100644 index 00000000..ecbb5599 --- /dev/null +++ b/javascript/batch_operations/README.md @@ -0,0 +1,121 @@ +# Batch Operations with node-postgres + +## Overview + +This code example demonstrates how to perform batch DELETE and UPDATE operations in Amazon Aurora DSQL +when working with datasets exceeding the 3,000-row transaction mutation limit. The example uses +[node-postgres](https://node-postgres.com/) with the +[Aurora DSQL Node.js Connector](https://github.com/awslabs/aurora-dsql-nodejs-connector) for automatic +IAM authentication. + +Two patterns are provided: + +- **Sequential**: A single-threaded loop that processes rows in configurable-size batches (default 1,000), + committing each batch as a separate transaction. +- **Parallel**: Multiple concurrent async workers each process a disjoint partition of the dataset using + `hashtext()` partitioning, with each worker running its own batch loop. + +Both patterns include OCC (Optimistic Concurrency Control) retry logic with exponential backoff. + +## About the code example + +Aurora DSQL limits each transaction to 3,000 row mutations. To DELETE or UPDATE more than 3,000 rows, +you must split the work into batches, each committed as a separate transaction. + +The parallel pattern partitions rows across workers using +`abs(hashtext(id::text)) % num_workers = worker_id`, ensuring workers operate on disjoint sets of rows +and avoid OCC conflicts with each other. + +⚠️ **Important** + +- Running this code might result in charges to your AWS account. +- Each batch is a separate transaction. A failure mid-way leaves the dataset partially modified. + Design your operations to be idempotent where possible. + +## Prerequisites + +- You must have an AWS account, and have your default credentials and AWS Region configured as described + in the [Globally configuring AWS SDKs and tools](https://docs.aws.amazon.com/sdkref/latest/guide/creds-config-files.html) guide. +- Node.js 18 or later. +- You must have an Aurora DSQL cluster. For information about creating a cluster, see the + [Getting started with Aurora DSQL](https://docs.aws.amazon.com/aurora-dsql/latest/userguide/getting-started.html) guide. + +## Set up + +Install the required packages: + +```bash +npm install +``` + +## Set up the test table + +Before running the examples, create and populate the test table. Aurora DSQL uses IAM authentication, +so you need to generate a fresh auth token each time you connect with `psql`: + +```bash +export CLUSTER_ENDPOINT="" +export CLUSTER_REGION="" + +# Generate a fresh auth token (expires in 3600 seconds) +export PGPASSWORD=$(aws dsql generate-db-connect-admin-auth-token \ + --hostname $CLUSTER_ENDPOINT \ + --region $CLUSTER_REGION \ + --expires-in 3600) + +psql "host=$CLUSTER_ENDPOINT dbname=postgres user=admin sslmode=verify-full sslrootcert=system" \ + -f batch_test_setup.sql +``` + +## Run the example + +Set environment variables for your cluster: + +```bash +# e.g. "admin" +export CLUSTER_USER="admin" + +# e.g. "foo0bar1baz2quux3quuux4.dsql.us-east-1.on.aws" +export CLUSTER_ENDPOINT="" +``` + +Run the demo: + +```bash +node src/main.js --endpoint "$CLUSTER_ENDPOINT" --user "$CLUSTER_USER" +``` + +### Command-line options + +| Option | Default | Description | +|--------|---------|-------------| +| `--endpoint` | (required) | Aurora DSQL cluster endpoint | +| `--user` | `admin` | Database user | +| `--batch-size` | `1000` | Rows per batch transaction (must be < 3000) | +| `--num-workers` | `4` | Number of parallel async workers | + +## Clean up + +After running the demo, drop the test table to avoid unnecessary storage: + +```bash +# Generate a fresh token if the previous one expired +export PGPASSWORD=$(aws dsql generate-db-connect-admin-auth-token \ + --hostname $CLUSTER_ENDPOINT \ + --region $CLUSTER_REGION \ + --expires-in 3600) + +psql "host=$CLUSTER_ENDPOINT dbname=postgres user=admin sslmode=verify-full sslrootcert=system" \ + -c "DROP TABLE IF EXISTS batch_test;" +``` + +## Additional resources + +- [Amazon Aurora DSQL Documentation](https://docs.aws.amazon.com/aurora-dsql/latest/userguide/) +- [Aurora DSQL Node.js Connector](https://github.com/awslabs/aurora-dsql-nodejs-connector) +- [node-postgres Documentation](https://node-postgres.com/) + +--- + +Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +SPDX-License-Identifier: MIT-0 diff --git a/javascript/batch_operations/batch_test_setup.sql b/javascript/batch_operations/batch_test_setup.sql new file mode 100644 index 00000000..3243506d --- /dev/null +++ b/javascript/batch_operations/batch_test_setup.sql @@ -0,0 +1,214 @@ +-- ============================================================================= +-- Sample table setup for Aurora DSQL batch operations +-- +-- This script creates a test table and populates it with 25,000 rows of sample +-- data distributed across 5 categories (~5,000 rows each). Each category is +-- used by a different demo operation: +-- +-- electronics → Sequential batch DELETE +-- clothing → Sequential batch UPDATE +-- food → Parallel batch DELETE +-- books → Parallel batch UPDATE +-- toys → (unused, remains in table) +-- +-- Aurora DSQL limits each transaction to 3,000 row mutations, so we insert +-- in batches of 1,000 rows. Run each INSERT as a separate transaction. +-- ============================================================================= + +-- Drop the table if it already exists +DROP TABLE IF EXISTS batch_test; + +-- Create the sample table. +-- Uses UUID primary key with gen_random_uuid() to minimize OCC contention, +-- following Aurora DSQL best practice of random keys. +CREATE TABLE batch_test ( + id UUID PRIMARY KEY DEFAULT gen_random_uuid(), + category VARCHAR(50) NOT NULL, + status VARCHAR(20) NOT NULL DEFAULT 'active', + value NUMERIC(10,2), + created_at TIMESTAMPTZ DEFAULT NOW(), + updated_at TIMESTAMPTZ DEFAULT NOW() +); + +-- Create an index on the category column. +CREATE INDEX ASYNC idx_batch_test_category ON batch_test (category); + +-- ============================================================================= +-- Populate the table with 25,000 rows of test data (25 batches of 1,000). +-- Each INSERT is 1,000 rows — run each as a separate transaction. +-- ============================================================================= + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); diff --git a/javascript/batch_operations/package-lock.json b/javascript/batch_operations/package-lock.json new file mode 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for Aurora DSQL", + "main": "src/main.js", + "scripts": { + "start": "node src/main.js", + "test": "jest --runInBand --detectOpenHandles --forceExit" + }, + "dependencies": { + "@aws/aurora-dsql-node-postgres-connector": "^0.1.8", + "pg": "^8.13.0" + }, + "devDependencies": { + "jest": "^30.3.0" + }, + "license": "MIT-0" +} diff --git a/javascript/batch_operations/src/batchDelete.js b/javascript/batch_operations/src/batchDelete.js new file mode 100644 index 00000000..fb1f580c --- /dev/null +++ b/javascript/batch_operations/src/batchDelete.js @@ -0,0 +1,175 @@ +// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +// SPDX-License-Identifier: MIT-0 + +/** + * Sequential and parallel batch DELETE for Aurora DSQL. + * + * Deletes rows matching a condition in configurable-size batches, committing + * each batch as a separate transaction to stay within the 3,000-row mutation + * limit. + */ + +const { MaxRetriesExceededError, executeWithRetry } = require("./occRetry"); + +/** Stop retrying a batch after this many consecutive OCC exhaustions. */ +const MAX_CONSECUTIVE_FAILURES = 10; + +/** + * Delete rows in batches, committing each batch as a separate transaction. + * + * If a single batch exhausts its OCC retries, the loop retries that batch + * with a fresh connection (up to MAX_CONSECUTIVE_FAILURES times) instead + * of aborting the entire operation. + * + * @param {import('pg').Pool} pool + * @param {string} table + * @param {string} condition - SQL WHERE clause (without `WHERE`). + * @param {number} [batchSize=1000] + * @param {number} [maxRetries=3] + * @param {number} [baseDelayMs=100] + * @returns {Promise} Total rows deleted. + */ +async function batchDelete(pool, table, condition, batchSize = 1000, maxRetries = 3, baseDelayMs = 100) { + let totalDeleted = 0; + let consecutiveFailures = 0; + + while (true) { + const client = await pool.connect(); + try { + const sql = `DELETE FROM ${table} WHERE id IN (SELECT id FROM ${table} WHERE ${condition} LIMIT ${batchSize})`; + + const deleted = await executeWithRetry( + client, + async (c) => { + await c.query("BEGIN"); + const result = await c.query(sql); + return result.rowCount; + }, + maxRetries, + baseDelayMs + ); + + await client.query("COMMIT"); + totalDeleted += deleted; + consecutiveFailures = 0; + console.log(`Deleted ${deleted} rows (total: ${totalDeleted})`); + + if (deleted === 0) break; + } catch (err) { + await client.query("ROLLBACK").catch(() => {}); + if (err instanceof MaxRetriesExceededError) { + consecutiveFailures++; + console.log( + `Batch OCC retries exhausted (${consecutiveFailures}/${MAX_CONSECUTIVE_FAILURES}), ` + + `retrying batch with fresh connection` + ); + if (consecutiveFailures >= MAX_CONSECUTIVE_FAILURES) throw err; + } else { + throw err; + } + } finally { + client.release(); + } + } + + // Post-verification: ensure no matching rows remain + const verifyClient = await pool.connect(); + try { + const res = await verifyClient.query(`SELECT COUNT(*) FROM ${table} WHERE ${condition}`); + const remaining = parseInt(res.rows[0].count, 10); + if (remaining > 0) { + console.log(`WARNING: ${remaining} rows still match condition after deleting ${totalDeleted} rows`); + } + } finally { + verifyClient.release(); + } + + return totalDeleted; +} + +/** + * Delete rows in parallel using multiple concurrent async workers. + * + * Partitions rows using `abs(hashtext(id::text)) % num_workers = worker_id`. + * + * @param {import('pg').Pool} pool + * @param {string} table + * @param {string} condition + * @param {number} [numWorkers=4] + * @param {number} [batchSize=1000] + * @param {number} [maxRetries=3] + * @param {number} [baseDelayMs=100] + * @returns {Promise} Total rows deleted. + */ +async function parallelBatchDelete( + pool, table, condition, numWorkers = 4, batchSize = 1000, maxRetries = 3, baseDelayMs = 100 +) { + async function worker(workerId) { + let totalDeleted = 0; + let consecutiveFailures = 0; + const partitionCondition = + `${condition} AND abs(hashtext(id::text)) % ${numWorkers} = ${workerId}`; + + while (true) { + const client = await pool.connect(); + try { + const sql = `DELETE FROM ${table} WHERE id IN (SELECT id FROM ${table} WHERE ${partitionCondition} LIMIT ${batchSize})`; + + const deleted = await executeWithRetry( + client, + async (c) => { + await c.query("BEGIN"); + const result = await c.query(sql); + return result.rowCount; + }, + maxRetries, + baseDelayMs + ); + + await client.query("COMMIT"); + totalDeleted += deleted; + consecutiveFailures = 0; + console.log(`Worker ${workerId}: Deleted ${deleted} rows (total: ${totalDeleted})`); + + if (deleted === 0) break; + } catch (err) { + await client.query("ROLLBACK").catch(() => {}); + if (err instanceof MaxRetriesExceededError) { + consecutiveFailures++; + console.log( + `Worker ${workerId}: Batch OCC retries exhausted (${consecutiveFailures}/${MAX_CONSECUTIVE_FAILURES}), ` + + `retrying batch with fresh connection` + ); + if (consecutiveFailures >= MAX_CONSECUTIVE_FAILURES) throw err; + } else { + throw err; + } + } finally { + client.release(); + } + } + + return totalDeleted; + } + + const workers = Array.from({ length: numWorkers }, (_, i) => worker(i)); + const results = await Promise.all(workers); + const total = results.reduce((sum, n) => sum + n, 0); + console.log(`Parallel delete complete: ${total} rows deleted by ${numWorkers} workers`); + + // Post-verification: ensure no matching rows remain (uses original condition, not partitioned) + const verifyClient = await pool.connect(); + try { + const res = await verifyClient.query(`SELECT COUNT(*) FROM ${table} WHERE ${condition}`); + const remaining = parseInt(res.rows[0].count, 10); + if (remaining > 0) { + console.log(`WARNING: ${remaining} rows still match condition after deleting ${total} rows`); + } + } finally { + verifyClient.release(); + } + + return total; +} + +module.exports = { batchDelete, parallelBatchDelete }; diff --git a/javascript/batch_operations/src/batchUpdate.js b/javascript/batch_operations/src/batchUpdate.js new file mode 100644 index 00000000..00b709c9 --- /dev/null +++ b/javascript/batch_operations/src/batchUpdate.js @@ -0,0 +1,187 @@ +// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +// SPDX-License-Identifier: MIT-0 + +/** + * Sequential and parallel batch UPDATE for Aurora DSQL. + * + * Updates rows matching a condition in configurable-size batches, committing + * each batch as a separate transaction to stay within the 3,000-row mutation + * limit. Uses a subquery to avoid reprocessing rows. + */ + +const { MaxRetriesExceededError, executeWithRetry } = require("./occRetry"); + +/** Stop retrying a batch after this many consecutive OCC exhaustions. */ +const MAX_CONSECUTIVE_FAILURES = 10; + +/** + * Update rows in batches, committing each batch as a separate transaction. + * + * If a single batch exhausts its OCC retries, the loop retries that batch + * with a fresh connection (up to MAX_CONSECUTIVE_FAILURES times) instead + * of aborting the entire operation. + * + * @param {import('pg').Pool} pool + * @param {string} table + * @param {string} setClause - SQL SET expressions (without `SET`). + * @param {string} condition - SQL WHERE clause (without `WHERE`). + * @param {number} [batchSize=1000] + * @param {number} [maxRetries=3] + * @param {number} [baseDelayMs=100] + * @returns {Promise} Total rows updated. + */ +async function batchUpdate(pool, table, setClause, condition, batchSize = 1000, maxRetries = 3, baseDelayMs = 100) { + let totalUpdated = 0; + let consecutiveFailures = 0; + + while (true) { + const client = await pool.connect(); + try { + const sql = `UPDATE ${table} SET ${setClause}, updated_at = NOW() + WHERE id IN ( + SELECT id FROM ${table} + WHERE ${condition} + LIMIT ${batchSize} + )`; + + const updated = await executeWithRetry( + client, + async (c) => { + await c.query("BEGIN"); + const result = await c.query(sql); + return result.rowCount; + }, + maxRetries, + baseDelayMs + ); + + await client.query("COMMIT"); + totalUpdated += updated; + consecutiveFailures = 0; + console.log(`Updated ${updated} rows (total: ${totalUpdated})`); + + if (updated === 0) break; + } catch (err) { + await client.query("ROLLBACK").catch(() => {}); + if (err instanceof MaxRetriesExceededError) { + consecutiveFailures++; + console.log( + `Batch OCC retries exhausted (${consecutiveFailures}/${MAX_CONSECUTIVE_FAILURES}), ` + + `retrying batch with fresh connection` + ); + if (consecutiveFailures >= MAX_CONSECUTIVE_FAILURES) throw err; + } else { + throw err; + } + } finally { + client.release(); + } + } + + // Post-verification: ensure no matching rows remain + const verifyClient = await pool.connect(); + try { + const res = await verifyClient.query(`SELECT COUNT(*) FROM ${table} WHERE ${condition}`); + const remaining = parseInt(res.rows[0].count, 10); + if (remaining > 0) { + console.log(`WARNING: ${remaining} rows still match condition after updating ${totalUpdated} rows`); + } + } finally { + verifyClient.release(); + } + + return totalUpdated; +} + +/** + * Update rows in parallel using multiple concurrent async workers. + * + * Partitions rows using `abs(hashtext(id::text)) % num_workers = worker_id`. + * + * @param {import('pg').Pool} pool + * @param {string} table + * @param {string} setClause + * @param {string} condition + * @param {number} [numWorkers=4] + * @param {number} [batchSize=1000] + * @param {number} [maxRetries=3] + * @param {number} [baseDelayMs=100] + * @returns {Promise} Total rows updated. + */ +async function parallelBatchUpdate( + pool, table, setClause, condition, numWorkers = 4, batchSize = 1000, maxRetries = 3, baseDelayMs = 100 +) { + async function worker(workerId) { + let totalUpdated = 0; + let consecutiveFailures = 0; + const partitionCondition = + `${condition} AND abs(hashtext(id::text)) % ${numWorkers} = ${workerId}`; + + while (true) { + const client = await pool.connect(); + try { + const sql = `UPDATE ${table} SET ${setClause}, updated_at = NOW() + WHERE id IN ( + SELECT id FROM ${table} + WHERE ${partitionCondition} + LIMIT ${batchSize} + )`; + + const updated = await executeWithRetry( + client, + async (c) => { + await c.query("BEGIN"); + const result = await c.query(sql); + return result.rowCount; + }, + maxRetries, + baseDelayMs + ); + + await client.query("COMMIT"); + totalUpdated += updated; + consecutiveFailures = 0; + console.log(`Worker ${workerId}: Updated ${updated} rows (total: ${totalUpdated})`); + + if (updated === 0) break; + } catch (err) { + await client.query("ROLLBACK").catch(() => {}); + if (err instanceof MaxRetriesExceededError) { + consecutiveFailures++; + console.log( + `Worker ${workerId}: Batch OCC retries exhausted (${consecutiveFailures}/${MAX_CONSECUTIVE_FAILURES}), ` + + `retrying batch with fresh connection` + ); + if (consecutiveFailures >= MAX_CONSECUTIVE_FAILURES) throw err; + } else { + throw err; + } + } finally { + client.release(); + } + } + + return totalUpdated; + } + + const workers = Array.from({ length: numWorkers }, (_, i) => worker(i)); + const results = await Promise.all(workers); + const total = results.reduce((sum, n) => sum + n, 0); + console.log(`Parallel update complete: ${total} rows updated by ${numWorkers} workers`); + + // Post-verification: ensure no matching rows remain (uses original condition, not partitioned) + const verifyClient = await pool.connect(); + try { + const res = await verifyClient.query(`SELECT COUNT(*) FROM ${table} WHERE ${condition}`); + const remaining = parseInt(res.rows[0].count, 10); + if (remaining > 0) { + console.log(`WARNING: ${remaining} rows still match condition after updating ${total} rows`); + } + } finally { + verifyClient.release(); + } + + return total; +} + +module.exports = { batchUpdate, parallelBatchUpdate }; diff --git a/javascript/batch_operations/src/main.js b/javascript/batch_operations/src/main.js new file mode 100644 index 00000000..56f49998 --- /dev/null +++ b/javascript/batch_operations/src/main.js @@ -0,0 +1,99 @@ +// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +// SPDX-License-Identifier: MIT-0 + +/** + * Main entry point for Aurora DSQL batch operations demo (Node.js). + * + * Usage: + * node main.js --endpoint [--user admin] \ + * [--batch-size 1000] [--num-workers 4] + */ + +const { AuroraDSQLPool } = require("@aws/aurora-dsql-node-postgres-connector"); +const { batchDelete, parallelBatchDelete } = require("./batchDelete"); +const { batchUpdate, parallelBatchUpdate } = require("./batchUpdate"); + +function parseArgs() { + const args = process.argv.slice(2); + const config = { endpoint: null, user: "admin", batchSize: 1000, numWorkers: 4 }; + + for (let i = 0; i < args.length; i++) { + switch (args[i]) { + case "--endpoint": config.endpoint = args[++i]; break; + case "--user": config.user = args[++i]; break; + case "--batch-size": config.batchSize = parseInt(args[++i], 10); break; + case "--num-workers": config.numWorkers = parseInt(args[++i], 10); break; + default: + console.error(`Unknown argument: ${args[i]}`); + process.exit(1); + } + } + + if (!config.endpoint) { + console.error( + "Usage: node main.js --endpoint " + + "[--user admin] [--batch-size 1000] [--num-workers 4]" + ); + process.exit(1); + } + return config; +} + +function createPool(endpoint, user, numWorkers) { + return new AuroraDSQLPool({ + host: endpoint, + user: user, + max: numWorkers, + idleTimeoutMillis: 30000, + connectionTimeoutMillis: 10000, + }); +} + +async function runOperation(label, fn) { + console.log(); + console.log("=".repeat(60)); + console.log(` ${label}`); + console.log("=".repeat(60)); + const start = Date.now(); + const total = await fn(); + const elapsed = ((Date.now() - start) / 1000).toFixed(2); + console.log(); + console.log(` Summary: ${total} rows affected in ${elapsed}s`); + console.log("=".repeat(60)); + return total; +} + +async function main() { + const config = parseArgs(); + const pool = createPool(config.endpoint, config.user, config.numWorkers); + const table = "batch_test"; + const { batchSize, numWorkers } = config; + + try { + await runOperation("Sequential Batch DELETE (category = 'electronics')", + () => batchDelete(pool, table, "category = 'electronics'", batchSize)); + + await runOperation("Sequential Batch UPDATE (clothing -> processed)", + () => batchUpdate(pool, table, "status = 'processed'", + "category = 'clothing' AND status != 'processed'", batchSize)); + + await runOperation(`Parallel Batch DELETE (category = 'food') [${numWorkers} workers]`, + () => parallelBatchDelete(pool, table, "category = 'food'", numWorkers, batchSize)); + + await runOperation(`Parallel Batch UPDATE (books -> archived) [${numWorkers} workers]`, + () => parallelBatchUpdate(pool, table, "status = 'archived'", + "category = 'books' AND status != 'archived'", numWorkers, batchSize)); + } finally { + await pool.end(); + console.log("\nConnection pool closed. Demo complete."); + } +} + +if (require.main === module) { + main().catch((err) => { + console.error("Fatal error:", err.message); + process.exit(1); + }); +} + +module.exports = { main }; diff --git a/javascript/batch_operations/src/occRetry.js b/javascript/batch_operations/src/occRetry.js new file mode 100644 index 00000000..e541cfd0 --- /dev/null +++ b/javascript/batch_operations/src/occRetry.js @@ -0,0 +1,56 @@ +// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +// SPDX-License-Identifier: MIT-0 + +/** + * OCC retry logic with exponential backoff for Aurora DSQL batch operations. + */ + +class MaxRetriesExceededError extends Error { + constructor(maxRetries) { + super(`Max retries exceeded: failed after ${maxRetries} retries`); + this.name = "MaxRetriesExceededError"; + this.maxRetries = maxRetries; + } +} + +function sleep(ms) { + return new Promise((resolve) => setTimeout(resolve, ms)); +} + +/** + * Execute a database operation with OCC conflict retry and exponential backoff. + * + * Retries on SQLSTATE 40001 (serialization failure) with exponential backoff. + * Each retry rolls back the current transaction before waiting. + * + * @param {import('pg').PoolClient} client - A node-postgres pool client. + * @param {(client: import('pg').PoolClient) => Promise<*>} operation - Async + * function that performs database work. Should NOT commit. + * @param {number} [maxRetries=3] - Maximum retry attempts. + * @param {number} [baseDelayMs=100] - Base delay in milliseconds for backoff. + * @returns {Promise<*>} The return value of `operation(client)`. + */ +async function executeWithRetry(client, operation, maxRetries = 3, baseDelayMs = 100) { + for (let attempt = 0; attempt <= maxRetries; attempt++) { + try { + return await operation(client); + } catch (e) { + if (e.code === "40001") { + await client.query("ROLLBACK"); + if (attempt >= maxRetries) { + throw new MaxRetriesExceededError(maxRetries); + } + const delayMs = baseDelayMs * Math.pow(2, attempt); + console.warn( + `OCC conflict, retry ${attempt + 1}/${maxRetries} after ${delayMs}ms` + ); + await sleep(delayMs); + } else { + throw e; + } + } + } + throw new MaxRetriesExceededError(maxRetries); +} + +module.exports = { MaxRetriesExceededError, executeWithRetry, sleep }; diff --git a/javascript/batch_operations/test/batch_operations.test.js b/javascript/batch_operations/test/batch_operations.test.js new file mode 100644 index 00000000..004059ac --- /dev/null +++ b/javascript/batch_operations/test/batch_operations.test.js @@ -0,0 +1,44 @@ +// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +// SPDX-License-Identifier: MIT-0 + +const path = require("path"); +const { execSync } = require("child_process"); + +const SETUP_SQL_PATH = path.join(__dirname, "..", "batch_test_setup.sql"); + +function extractRegion(endpoint) { + const m = endpoint.match(/\.dsql\.([^.]+)\.on\.aws/); + if (!m) throw new Error(`Cannot extract region from endpoint: ${endpoint}`); + return m[1]; +} + +function seedDatabase() { + const endpoint = process.env.CLUSTER_ENDPOINT; + if (!endpoint) throw new Error("CLUSTER_ENDPOINT environment variable is required"); + const user = process.env.CLUSTER_USER || "admin"; + const region = extractRegion(endpoint); + + const token = execSync( + `aws dsql generate-db-connect-admin-auth-token --hostname ${endpoint} --region ${region} --expires-in 3600`, + { encoding: "utf8" } + ).trim(); + + execSync( + `psql "host=${endpoint} dbname=postgres user=${user} sslmode=verify-full sslrootcert=system connect_timeout=10" -f ${SETUP_SQL_PATH}`, + { env: { ...process.env, PGPASSWORD: token }, stdio: "inherit" } + ); +} + +beforeAll(() => { + seedDatabase(); +}, 120000); + +test("Batch operations demo runs successfully", async () => { + // Set process.argv so main()'s parseArgs picks up the endpoint + const endpoint = process.env.CLUSTER_ENDPOINT; + const user = process.env.CLUSTER_USER || "admin"; + process.argv = ["node", "main.js", "--endpoint", endpoint, "--user", user]; + + const { main } = require("../src/main"); + await main(); +}, 120000); diff --git a/javascript/node-postgres/package-lock.json b/javascript/node-postgres/package-lock.json index 69d1e3d9..879dd6f5 100644 --- a/javascript/node-postgres/package-lock.json +++ b/javascript/node-postgres/package-lock.json @@ -1,17 +1,16 @@ { - 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The example uses +[psycopg2](https://www.psycopg.org/docs/) with the +[Aurora DSQL Python Connector](https://github.com/awslabs/aurora-dsql-python-connector) for automatic +IAM authentication, and connection pooling. + +Two patterns are provided: + +- **Sequential**: A single-threaded loop that processes rows in configurable-size batches (default 1,000), + committing each batch as a separate transaction. +- **Parallel**: Multiple worker threads each process a disjoint partition of the dataset concurrently using + `hashtext()` partitioning, with each worker running its own batch loop. + +Both patterns include OCC (Optimistic Concurrency Control) retry logic with exponential backoff. + +## About the code example + +Aurora DSQL limits each transaction to 3,000 row mutations. To DELETE or UPDATE more than 3,000 rows, +you must split the work into batches, each committed as a separate transaction. + +The parallel pattern partitions rows across worker threads using +`abs(hashtext(id::text)) % num_workers = worker_id`, ensuring workers operate on disjoint sets of rows +and avoid OCC conflicts with each other. + +Connection pooling uses `AuroraDSQLThreadedConnectionPool` from the Aurora DSQL Python Connector, which +automatically handles IAM token refresh for long-running batch jobs. + +⚠️ **Important** + +- Running this code might result in charges to your AWS account. +- Each batch is a separate transaction. A failure mid-way leaves the dataset partially modified. + Design your operations to be idempotent where possible. + +## Prerequisites + +- You must have an AWS account, and have your default credentials and AWS Region configured as described + in the [Globally configuring AWS SDKs and tools](https://docs.aws.amazon.com/sdkref/latest/guide/creds-config-files.html) guide. +- Python 3.8 or later. +- You must have an Aurora DSQL cluster. For information about creating a cluster, see the + [Getting started with Aurora DSQL](https://docs.aws.amazon.com/aurora-dsql/latest/userguide/getting-started.html) guide. + +## Set up + +Create and activate a Python virtual environment: + +```bash +python3 -m venv .venv +source .venv/bin/activate +``` + +Install the required packages: + +```bash +pip install -r requirements.txt +``` + +## Set up the test table + +Before running the examples, create and populate the test table. Run each INSERT as a separate +transaction (each block of 1,000 rows). Aurora DSQL uses IAM authentication, so you need to +generate a fresh auth token each time you connect with `psql`: + +```bash +export CLUSTER_ENDPOINT="" +export CLUSTER_REGION="" + +# Generate a fresh auth token (expires in 3600 seconds) +export PGPASSWORD=$(aws dsql generate-db-connect-admin-auth-token \ + --hostname $CLUSTER_ENDPOINT \ + --region $CLUSTER_REGION \ + --expires-in 3600) + +psql "host=$CLUSTER_ENDPOINT dbname=postgres user=admin sslmode=verify-full sslrootcert=system" \ + -f batch_test_setup.sql +``` + +## Run the example + +Set environment variables for your cluster: + +```bash +# e.g. "admin" +export CLUSTER_USER="admin" + +# e.g. "foo0bar1baz2quux3quuux4.dsql.us-east-1.on.aws" +export CLUSTER_ENDPOINT="" +``` + +Run the demo: + +```bash +python src/main.py --endpoint "$CLUSTER_ENDPOINT" --user "$CLUSTER_USER" +``` + +### Command-line options + +| Option | Default | Description | +|--------|---------|-------------| +| `--endpoint` | (required) | Aurora DSQL cluster endpoint | +| `--user` | `admin` | Database user | +| `--batch-size` | `1000` | Rows per batch transaction (must be < 3000) | +| `--num-workers` | `4` | Number of parallel worker threads | + +The demo runs the following sequence: + +1. Sequential batch DELETE (all `electronics` rows) +2. Sequential batch UPDATE (`clothing` → `processed`) +3. Parallel batch DELETE (all `food` rows) with N workers +4. Parallel batch UPDATE (`books` → `archived`) with N workers + +## Clean up + +After running the demo, drop the test table to avoid unnecessary storage: + +```bash +# Generate a fresh token if the previous one expired +export PGPASSWORD=$(aws dsql generate-db-connect-admin-auth-token \ + --hostname $CLUSTER_ENDPOINT \ + --region $CLUSTER_REGION \ + --expires-in 3600) + +psql "host=$CLUSTER_ENDPOINT dbname=postgres user=admin sslmode=verify-full sslrootcert=system" \ + -c "DROP TABLE IF EXISTS batch_test;" +``` + +## Additional resources + +- [Amazon Aurora DSQL Documentation](https://docs.aws.amazon.com/aurora-dsql/latest/userguide/) +- [Aurora DSQL Python Connector](https://github.com/awslabs/aurora-dsql-python-connector) +- [Psycopg2 Documentation](https://www.psycopg.org/docs/) +- [Batch operations in Aurora DSQL](https://docs.aws.amazon.com/aurora-dsql/latest/userguide/batch-operations.html) + +--- + +Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +SPDX-License-Identifier: MIT-0 diff --git a/python/batch_operations/batch_test_setup.sql b/python/batch_operations/batch_test_setup.sql new file mode 100644 index 00000000..3243506d --- /dev/null +++ b/python/batch_operations/batch_test_setup.sql @@ -0,0 +1,214 @@ +-- ============================================================================= +-- Sample table setup for Aurora DSQL batch operations +-- +-- This script creates a test table and populates it with 25,000 rows of sample +-- data distributed across 5 categories (~5,000 rows each). Each category is +-- used by a different demo operation: +-- +-- electronics → Sequential batch DELETE +-- clothing → Sequential batch UPDATE +-- food → Parallel batch DELETE +-- books → Parallel batch UPDATE +-- toys → (unused, remains in table) +-- +-- Aurora DSQL limits each transaction to 3,000 row mutations, so we insert +-- in batches of 1,000 rows. Run each INSERT as a separate transaction. +-- ============================================================================= + +-- Drop the table if it already exists +DROP TABLE IF EXISTS batch_test; + +-- Create the sample table. +-- Uses UUID primary key with gen_random_uuid() to minimize OCC contention, +-- following Aurora DSQL best practice of random keys. +CREATE TABLE batch_test ( + id UUID PRIMARY KEY DEFAULT gen_random_uuid(), + category VARCHAR(50) NOT NULL, + status VARCHAR(20) NOT NULL DEFAULT 'active', + value NUMERIC(10,2), + created_at TIMESTAMPTZ DEFAULT NOW(), + updated_at TIMESTAMPTZ DEFAULT NOW() +); + +-- Create an index on the category column. +CREATE INDEX ASYNC idx_batch_test_category ON batch_test (category); + +-- ============================================================================= +-- Populate the table with 25,000 rows of test data (25 batches of 1,000). +-- Each INSERT is 1,000 rows — run each as a separate transaction. +-- ============================================================================= + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); + +INSERT INTO batch_test (category, status, value) +SELECT + (ARRAY['electronics', 'clothing', 'food', 'books', 'toys'])[floor(random() * 5 + 1)], + 'active', + round((random() * 1000)::numeric, 2) +FROM generate_series(1, 1000); diff --git a/python/batch_operations/src/batch_delete.py b/python/batch_operations/src/batch_delete.py new file mode 100644 index 00000000..783261f0 --- /dev/null +++ b/python/batch_operations/src/batch_delete.py @@ -0,0 +1,98 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: MIT-0 + +"""Sequential and parallel batch DELETE for Aurora DSQL. + +Deletes rows matching a condition in configurable-size batches, committing each +batch as a separate transaction to stay within the 3,000-row mutation limit. +Uses OCC retry logic with exponential backoff for serialization conflicts. + +The parallel variant partitions rows across worker threads using +``abs(hashtext(id::text)) % num_workers`` so that each worker operates on a +disjoint subset, avoiding OCC conflicts between workers. +""" + +import threading + +from occ_retry import MaxRetriesExceeded, execute_with_retry + +# Stop retrying a batch after this many consecutive OCC exhaustions. +MAX_CONSECUTIVE_FAILURES = 10 + + +def batch_delete(pool, table, condition, batch_size=1000, max_retries=3, base_delay_ms=100): + """Delete rows in batches, committing each batch as a separate transaction. + + If a single batch exhausts its OCC retries, the loop retries that batch + with a fresh connection (up to ``MAX_CONSECUTIVE_FAILURES`` times) instead + of aborting the entire operation. Previously committed batches are durable, + so forward progress is preserved. + + Args: + pool: A ``dsql.AuroraDSQLThreadedConnectionPool`` instance. + table: Name of the table to delete from. + condition: SQL WHERE clause (without the ``WHERE`` keyword). + batch_size: Number of rows to delete per transaction (default 1,000). + max_retries: Maximum OCC retry attempts per batch (default 3). + base_delay_ms: Base delay in milliseconds for exponential backoff (default 100). + + Returns: + Total number of rows deleted across all batches. + """ + total_deleted = 0 + consecutive_failures = 0 + + while True: + conn = pool.getconn() + try: + + def delete_batch(conn): + with conn.cursor() as cur: + cur.execute( + f"""DELETE FROM {table} + WHERE id IN ( + SELECT id FROM {table} + WHERE {condition} + LIMIT {batch_size} + )""" + ) + return cur.rowcount + + deleted = execute_with_retry( + conn, delete_batch, max_retries=max_retries, base_delay_ms=base_delay_ms + ) + conn.commit() + total_deleted += deleted + consecutive_failures = 0 # reset on success + print(f"Deleted {deleted} rows (total: {total_deleted})") + + if deleted == 0: + break + except MaxRetriesExceeded: + conn.rollback() + consecutive_failures += 1 + print( + f"Batch OCC retries exhausted ({consecutive_failures}/" + f"{MAX_CONSECUTIVE_FAILURES}), retrying batch with fresh connection" + ) + if consecutive_failures >= MAX_CONSECUTIVE_FAILURES: + raise + except Exception: + conn.rollback() + raise + finally: + pool.putconn(conn) + + # Post-verification: ensure no matching rows remain + conn = pool.getconn() + try: + with conn.cursor() as cur: + cur.execute(f"SELECT COUNT(*) FROM {table} WHERE {condition}") + remaining = cur.fetchone()[0] + conn.commit() + if remaining > 0: + print(f"WARNING: {remaining} rows still match condition after deleting {total_deleted} rows") + finally: + pool.putconn(conn) + + return total_deleted diff --git a/python/batch_operations/src/batch_update.py b/python/batch_operations/src/batch_update.py new file mode 100644 index 00000000..7200592a --- /dev/null +++ b/python/batch_operations/src/batch_update.py @@ -0,0 +1,98 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: MIT-0 + +"""Sequential batch UPDATE for Aurora DSQL. + +Updates rows matching a condition in configurable-size batches, committing each +batch as a separate transaction to stay within the 3,000-row mutation limit. +Uses a subquery pattern to select rows not yet updated, and sets updated_at = +NOW() to track processed rows. +""" + +from occ_retry import MaxRetriesExceeded, execute_with_retry + +# Stop retrying a batch after this many consecutive OCC exhaustions. +MAX_CONSECUTIVE_FAILURES = 10 + + +def batch_update(pool, table, set_clause, condition, batch_size=1000, max_retries=3, base_delay_ms=100): + """Update rows in batches, committing each batch as a separate transaction. + + Uses a subquery to select rows not yet updated and sets ``updated_at = NOW()`` + on each batch to track which rows have been processed. + + If a single batch exhausts its OCC retries, the loop retries that batch + with a fresh connection (up to ``MAX_CONSECUTIVE_FAILURES`` times) instead + of aborting the entire operation. + + Args: + pool: A ``dsql.AuroraDSQLThreadedConnectionPool`` instance. + table: Name of the table to update. + set_clause: SQL SET expressions (without the ``SET`` keyword), + e.g. ``"status = 'processed'"``. + condition: SQL WHERE clause (without the ``WHERE`` keyword) that + identifies rows needing update. + batch_size: Number of rows to update per transaction (default 1,000). + max_retries: Maximum OCC retry attempts per batch (default 3). + base_delay_ms: Base delay in milliseconds for exponential backoff (default 100). + + Returns: + Total number of rows updated across all batches. + """ + total_updated = 0 + consecutive_failures = 0 + + while True: + conn = pool.getconn() + try: + + def update_batch(conn): + with conn.cursor() as cur: + cur.execute( + f"""UPDATE {table} SET {set_clause}, updated_at = NOW() + WHERE id IN ( + SELECT id FROM {table} + WHERE {condition} + LIMIT {batch_size} + )""" + ) + return cur.rowcount + + updated = execute_with_retry( + conn, update_batch, max_retries=max_retries, base_delay_ms=base_delay_ms + ) + conn.commit() + total_updated += updated + consecutive_failures = 0 # reset on success + print(f"Updated {updated} rows (total: {total_updated})") + + if updated == 0: + break + except MaxRetriesExceeded: + conn.rollback() + consecutive_failures += 1 + print( + f"Batch OCC retries exhausted ({consecutive_failures}/" + f"{MAX_CONSECUTIVE_FAILURES}), retrying batch with fresh connection" + ) + if consecutive_failures >= MAX_CONSECUTIVE_FAILURES: + raise + except Exception: + conn.rollback() + raise + finally: + pool.putconn(conn) + + # Post-verification: ensure no matching rows remain + conn = pool.getconn() + try: + with conn.cursor() as cur: + cur.execute(f"SELECT COUNT(*) FROM {table} WHERE {condition}") + remaining = cur.fetchone()[0] + conn.commit() + if remaining > 0: + print(f"WARNING: {remaining} rows still match condition after updating {total_updated} rows") + finally: + pool.putconn(conn) + + return total_updated diff --git a/python/batch_operations/src/main.py b/python/batch_operations/src/main.py new file mode 100644 index 00000000..218f4f93 --- /dev/null +++ b/python/batch_operations/src/main.py @@ -0,0 +1,129 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: MIT-0 + +"""Main entry point for Aurora DSQL batch operations demo. + +Demonstrates sequential and parallel batch DELETE and UPDATE operations +against an Aurora DSQL cluster, with OCC retry logic and connection pooling. + +Usage: + python main.py --endpoint [--user admin] \ + [--batch-size 1000] [--num-workers 4] +""" + +import argparse +import time + +import aurora_dsql_psycopg2 as dsql + +from batch_delete import batch_delete +from batch_update import batch_update +from parallel_batch_delete import parallel_batch_delete +from parallel_batch_update import parallel_batch_update + + +def parse_args(): + """Parse command-line arguments.""" + parser = argparse.ArgumentParser( + description="Aurora DSQL batch operations demo" + ) + parser.add_argument( + "--endpoint", required=True, help="Aurora DSQL cluster endpoint" + ) + parser.add_argument( + "--user", default="admin", help="Database user (default: admin)" + ) + parser.add_argument( + "--batch-size", + type=int, + default=1000, + help="Rows per batch transaction (default: 1000)", + ) + parser.add_argument( + "--num-workers", + type=int, + default=4, + help="Number of parallel workers (default: 4)", + ) + return parser.parse_args() + + +def create_pool(endpoint, user, num_workers): + """Create a threaded connection pool sized for parallel workers.""" + conn_params = { + "user": user, + "host": endpoint, + "connect_timeout": 10, + } + return dsql.AuroraDSQLThreadedConnectionPool( + minconn=num_workers, + maxconn=num_workers, + **conn_params, + ) + + +def run_operation(label, func, *args, **kwargs): + """Run a batch operation, print a summary with elapsed time.""" + print(f"\n{'=' * 60}") + print(f" {label}") + print(f"{'=' * 60}") + start = time.time() + total = func(*args, **kwargs) + elapsed = time.time() - start + print(f"\n Summary: {total} rows affected in {elapsed:.2f}s") + print(f"{'=' * 60}") + return total + + +def main(): + args = parse_args() + pool = create_pool(args.endpoint, args.user, args.num_workers) + + table = "batch_test" + batch_size = args.batch_size + num_workers = args.num_workers + + try: + # 1. Sequential batch DELETE — delete all 'electronics' rows + run_operation( + "Sequential Batch DELETE (category = 'electronics')", + batch_delete, + pool, table, "category = 'electronics'", + batch_size=batch_size, + ) + + # 2. Sequential batch UPDATE — update 'clothing' status to 'processed' + run_operation( + "Sequential Batch UPDATE (clothing -> processed)", + batch_update, + pool, table, "status = 'processed'", + "category = 'clothing' AND status != 'processed'", + batch_size=batch_size, + ) + + # 3. Parallel batch DELETE — delete all 'food' rows + run_operation( + f"Parallel Batch DELETE (category = 'food') [{num_workers} workers]", + parallel_batch_delete, + pool, table, "category = 'food'", + num_workers=num_workers, + batch_size=batch_size, + ) + + # 4. Parallel batch UPDATE — update 'books' status to 'archived' + run_operation( + f"Parallel Batch UPDATE (books -> archived) [{num_workers} workers]", + parallel_batch_update, + pool, table, "status = 'archived'", + "category = 'books' AND status != 'archived'", + num_workers=num_workers, + batch_size=batch_size, + ) + + finally: + pool.closeall() + print("\nConnection pool closed. Demo complete.") + + +if __name__ == "__main__": + main() diff --git a/python/batch_operations/src/occ_retry.py b/python/batch_operations/src/occ_retry.py new file mode 100644 index 00000000..03c3fa1b --- /dev/null +++ b/python/batch_operations/src/occ_retry.py @@ -0,0 +1,61 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: MIT-0 + +"""OCC retry logic with exponential backoff for Aurora DSQL batch operations.""" + +import logging +import time + +import psycopg2 +import psycopg2.errors + +logger = logging.getLogger(__name__) + + +class MaxRetriesExceeded(Exception): + """Raised when a batch operation exceeds the maximum number of OCC retries.""" + + def __init__(self, max_retries): + super().__init__( + f"Max retries exceeded: failed after {max_retries} retries" + ) + self.max_retries = max_retries + + +def execute_with_retry(connection, operation, max_retries=3, base_delay_ms=100): + """Execute a database operation with OCC conflict retry and exponential backoff. + + Runs ``operation(connection)`` and retries on serialization failures + (SQLSTATE 40001) using exponential backoff. Each retry rolls back the + current transaction before waiting. + + Args: + connection: A psycopg2 connection object. + operation: A callable that accepts a connection and performs database + work. It should NOT commit — the caller is responsible for commits. + max_retries: Maximum number of retry attempts (default 3). + base_delay_ms: Base delay in milliseconds for exponential backoff + (default 100). + + Returns: + The return value of ``operation(connection)``. + + Raises: + MaxRetriesExceeded: If the operation fails with OCC conflicts after + ``max_retries`` retry attempts. + """ + for attempt in range(max_retries + 1): + try: + return operation(connection) + except psycopg2.errors.SerializationFailure: + connection.rollback() + if attempt >= max_retries: + raise MaxRetriesExceeded(max_retries) + delay_ms = base_delay_ms * (2 ** attempt) + logger.warning( + "OCC conflict, retry %d/%d after %dms", + attempt + 1, + max_retries, + delay_ms, + ) + time.sleep(delay_ms / 1000.0) diff --git a/python/batch_operations/src/parallel_batch_delete.py b/python/batch_operations/src/parallel_batch_delete.py new file mode 100644 index 00000000..5fbb9213 --- /dev/null +++ b/python/batch_operations/src/parallel_batch_delete.py @@ -0,0 +1,126 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: MIT-0 + +"""Parallel batch DELETE for Aurora DSQL. + +Partitions rows across worker threads using ``abs(hashtext(id::text)) % num_workers`` +so that each worker operates on a disjoint subset, avoiding OCC conflicts between +workers. Each worker runs a sequential batch-delete loop on its partition. +""" + +import threading + +from occ_retry import MaxRetriesExceeded, execute_with_retry + +# Stop retrying a batch after this many consecutive OCC exhaustions. +MAX_CONSECUTIVE_FAILURES = 10 + + +def parallel_batch_delete( + pool, table, condition, num_workers=4, batch_size=1000, max_retries=3, base_delay_ms=100 +): + """Delete rows in parallel using multiple worker threads. + + Each worker retries a batch with a fresh connection if OCC retries are + exhausted, up to ``MAX_CONSECUTIVE_FAILURES`` times before giving up. + + Args: + pool: A ``dsql.AuroraDSQLThreadedConnectionPool`` instance sized to at + least *num_workers* connections. + table: Name of the table to delete from. + condition: SQL WHERE clause (without the ``WHERE`` keyword). + num_workers: Number of parallel worker threads (default 4). + batch_size: Number of rows to delete per transaction (default 1,000). + max_retries: Maximum OCC retry attempts per batch (default 3). + base_delay_ms: Base delay in milliseconds for exponential backoff (default 100). + + Returns: + Total number of rows deleted across all workers. + """ + results = [0] * num_workers + errors = [None] * num_workers + + def worker(worker_id): + total_deleted = 0 + consecutive_failures = 0 + partition_condition = ( + f"{condition} AND abs(hashtext(id::text)) % {num_workers} = {worker_id}" + ) + + while True: + conn = pool.getconn() + try: + + def delete_batch(conn): + with conn.cursor() as cur: + cur.execute( + f"""DELETE FROM {table} + WHERE id IN ( + SELECT id FROM {table} + WHERE {partition_condition} + LIMIT {batch_size} + )""" + ) + return cur.rowcount + + deleted = execute_with_retry( + conn, delete_batch, max_retries=max_retries, base_delay_ms=base_delay_ms + ) + conn.commit() + total_deleted += deleted + consecutive_failures = 0 + print( + f"Worker {worker_id}: Deleted {deleted} rows " + f"(total: {total_deleted})" + ) + + if deleted == 0: + break + except MaxRetriesExceeded: + conn.rollback() + consecutive_failures += 1 + print( + f"Worker {worker_id}: Batch OCC retries exhausted " + f"({consecutive_failures}/{MAX_CONSECUTIVE_FAILURES}), " + f"retrying batch with fresh connection" + ) + if consecutive_failures >= MAX_CONSECUTIVE_FAILURES: + errors[worker_id] = MaxRetriesExceeded(max_retries) + break + except Exception as exc: + conn.rollback() + errors[worker_id] = exc + break + finally: + pool.putconn(conn) + + results[worker_id] = total_deleted + + threads = [ + threading.Thread(target=worker, args=(i,)) for i in range(num_workers) + ] + for t in threads: + t.start() + for t in threads: + t.join() + + for err in errors: + if err is not None: + raise err + + total = sum(results) + print(f"Parallel delete complete: {total} rows deleted by {num_workers} workers") + + # Post-verification: ensure no matching rows remain (uses original condition, not partitioned) + conn = pool.getconn() + try: + with conn.cursor() as cur: + cur.execute(f"SELECT COUNT(*) FROM {table} WHERE {condition}") + remaining = cur.fetchone()[0] + conn.commit() + if remaining > 0: + print(f"WARNING: {remaining} rows still match condition after deleting {total} rows") + finally: + pool.putconn(conn) + + return total diff --git a/python/batch_operations/src/parallel_batch_update.py b/python/batch_operations/src/parallel_batch_update.py new file mode 100644 index 00000000..f1037a38 --- /dev/null +++ b/python/batch_operations/src/parallel_batch_update.py @@ -0,0 +1,127 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: MIT-0 + +"""Parallel batch UPDATE for Aurora DSQL. + +Partitions rows across worker threads using ``abs(hashtext(id::text)) % num_workers`` +so that each worker operates on a disjoint subset, avoiding OCC conflicts between +workers. Each worker runs a sequential batch-update loop on its partition. +""" + +import threading + +from occ_retry import MaxRetriesExceeded, execute_with_retry + +# Stop retrying a batch after this many consecutive OCC exhaustions. +MAX_CONSECUTIVE_FAILURES = 10 + + +def parallel_batch_update( + pool, table, set_clause, condition, num_workers=4, batch_size=1000, max_retries=3, base_delay_ms=100 +): + """Update rows in parallel using multiple worker threads. + + Each worker retries a batch with a fresh connection if OCC retries are + exhausted, up to ``MAX_CONSECUTIVE_FAILURES`` times before giving up. + + Args: + pool: A ``dsql.AuroraDSQLThreadedConnectionPool`` instance sized to at + least *num_workers* connections. + table: Name of the table to update. + set_clause: SQL SET expressions (without the ``SET`` keyword). + condition: SQL WHERE clause (without the ``WHERE`` keyword). + num_workers: Number of parallel worker threads (default 4). + batch_size: Number of rows to update per transaction (default 1,000). + max_retries: Maximum OCC retry attempts per batch (default 3). + base_delay_ms: Base delay in milliseconds for exponential backoff (default 100). + + Returns: + Total number of rows updated across all workers. + """ + results = [0] * num_workers + errors = [None] * num_workers + + def worker(worker_id): + total_updated = 0 + consecutive_failures = 0 + partition_condition = ( + f"{condition} AND abs(hashtext(id::text)) % {num_workers} = {worker_id}" + ) + + while True: + conn = pool.getconn() + try: + + def update_batch(conn): + with conn.cursor() as cur: + cur.execute( + f"""UPDATE {table} SET {set_clause}, updated_at = NOW() + WHERE id IN ( + SELECT id FROM {table} + WHERE {partition_condition} + LIMIT {batch_size} + )""" + ) + return cur.rowcount + + updated = execute_with_retry( + conn, update_batch, max_retries=max_retries, base_delay_ms=base_delay_ms + ) + conn.commit() + total_updated += updated + consecutive_failures = 0 + print( + f"Worker {worker_id}: Updated {updated} rows " + f"(total: {total_updated})" + ) + + if updated == 0: + break + except MaxRetriesExceeded: + conn.rollback() + consecutive_failures += 1 + print( + f"Worker {worker_id}: Batch OCC retries exhausted " + f"({consecutive_failures}/{MAX_CONSECUTIVE_FAILURES}), " + f"retrying batch with fresh connection" + ) + if consecutive_failures >= MAX_CONSECUTIVE_FAILURES: + errors[worker_id] = MaxRetriesExceeded(max_retries) + break + except Exception as exc: + conn.rollback() + errors[worker_id] = exc + break + finally: + pool.putconn(conn) + + results[worker_id] = total_updated + + threads = [ + threading.Thread(target=worker, args=(i,)) for i in range(num_workers) + ] + for t in threads: + t.start() + for t in threads: + t.join() + + for err in errors: + if err is not None: + raise err + + total = sum(results) + print(f"Parallel update complete: {total} rows updated by {num_workers} workers") + + # Post-verification: ensure no matching rows remain (uses original condition, not partitioned) + conn = pool.getconn() + try: + with conn.cursor() as cur: + cur.execute(f"SELECT COUNT(*) FROM {table} WHERE {condition}") + remaining = cur.fetchone()[0] + conn.commit() + if remaining > 0: + print(f"WARNING: {remaining} rows still match condition after updating {total} rows") + finally: + pool.putconn(conn) + + return total diff --git a/python/batch_operations/test/test_batch_operations.py b/python/batch_operations/test/test_batch_operations.py new file mode 100644 index 00000000..d35aee85 --- /dev/null +++ b/python/batch_operations/test/test_batch_operations.py @@ -0,0 +1,74 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: MIT-0 + +import os +import re +import subprocess +import sys + +import pytest + + +SETUP_SQL_PATH = os.path.join(os.path.dirname(__file__), "..", "batch_test_setup.sql") + + +def get_endpoint(): + ep = os.environ.get("CLUSTER_ENDPOINT") + assert ep, "CLUSTER_ENDPOINT environment variable is required" + return ep + + +def get_user(): + return os.environ.get("CLUSTER_USER", "admin") + + +def extract_region(endpoint): + """Extract AWS region from a DSQL endpoint like 'xxx.dsql.us-east-1.on.aws'.""" + m = re.search(r"\.dsql\.([^.]+)\.on\.aws", endpoint) + assert m, f"Cannot extract region from endpoint: {endpoint}" + return m.group(1) + + +def run_setup_sql(endpoint, user): + """Run batch_test_setup.sql via psql.""" + region = extract_region(endpoint) + token = subprocess.check_output( + [ + "aws", "dsql", "generate-db-connect-admin-auth-token", + "--hostname", endpoint, + "--region", region, + "--expires-in", "3600", + ], + text=True, + ).strip() + + subprocess.run( + [ + "psql", + f"host={endpoint} dbname=postgres user={user} sslmode=verify-full sslrootcert=system connect_timeout=10", + "-f", SETUP_SQL_PATH, + ], + env={**os.environ, "PGPASSWORD": token}, + check=True, + ) + + +def test_batch_operations(): + """Integration test: seeds the table, then runs the full batch operations demo. + + Requires CLUSTER_ENDPOINT environment variable (and optionally CLUSTER_USER). + """ + endpoint = get_endpoint() + user = get_user() + + # Seed the test table + run_setup_sql(endpoint, user) + + # Set sys.argv so main()'s argparse picks up the endpoint + sys.argv = ["main.py", "--endpoint", endpoint, "--user", user] + + from main import main + try: + main() + except Exception as e: + pytest.fail(f"Unexpected exception: {e}")