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Clueso

Instructions

To build, run:

./gradlew clean buildDocker

To run integration tests – requires Docker:

./gradlew clean test

Clueso Tool

To build Clueso Tool, invoke:

./gradlew buildTool

The tool will be available under ./docker/images/docker-spark/base/clueso/bin

Table Compactor Tool

Format: ./table-compactor.sh <path/to/application.conf> <master-spark-URL> <num parquet files per partition> [<bucket name>] [<forceCompaction>]

Parameters

  1. path to application.conf, that specifies S3 connection settings and compaction settings
  2. Spark Master URL, e.g. spark://spark-master:7077
  3. number of partitions – set this value to the same as number of spark executors
  4. Optional bucket name – name of bucket to compact, if none set, will compact all
  5. Optional force – if true, compacts the bucket regardless of number of subpartitions

Run Example

Run compaction with 20:

./table-compactor.sh application.conf 20

Add it to Cronjob

To run compaction every 24 hours at 1AM:

Run crontab -e and select your editor:

Add this to the file

0 1 * * * `./table-compactor.sh application.conf 5`

Compact a specific bucket

To run compaction on a specific bucket:

./table-compactor.sh application.conf 20 myFatBucket

Landing Populator Tool

/!\ Warning /!\

This tool wipes all the data in the selected bucket's landing folder, so use with caution.

This tool generates fake metadata and can be used prior to a performance test.

Format: ./landing-populator-tool.sh application.conf <bucketName> <num records> <num parquet files>

Parameters

  1. path to application.conf, that specifies S3 connection settings
  2. bucket name – name of bucket of the generated records
  3. number of records – number of records to be generated
  4. number of parquet files – number of parquet files to write

Run this command to generate 100k metadata entries for bucket high-traffic-bucket in landing evenly spread across 100 parquet files.

./landing-populator-tool.sh application.conf high-traffic-bucket 100000 100

Info Tool

This tool can report on the number of search metadata files in persistent layer (S3). This includes average file size of Parquet files, number of records in metadata for a specific bucket and number of parquet files in total for that bucket.

./info.sh application.conf <bucketName> [loop=true|false]

By selecting loop=true, it will periodically poll and send information to graphite.

Please set GRAPHITE_HOST and GRAPHITE_PORT

Output will be similar to:

17/11/17 21:33:21 INFO MetadataStorageInfoTool$: search_metadata.staging.try1.parquet_file_count 0 1510954401

17/11/17 21:33:21 INFO MetadataStorageInfoTool$: search_metadata.landing.try1.avg_file_size 1872098 1510954401

17/11/17 21:33:21 INFO MetadataStorageInfoTool$: search_metadata.staging.try1.avg_file_size 0 1510954401

17/11/17 21:33:21 INFO MetadataStorageInfoTool$: search_metadata.landing.try1.total_file_size 134791056 1510954401

17/11/17 21:33:21 INFO MetadataStorageInfoTool$: search_metadata.staging.try1.total_file_size 0 1510954401

17/11/17 21:33:21 INFO MetadataStorageInfoTool$: search_metadata.landing.try1.record_count 7000000 1510954401

17/11/17 21:33:21 INFO MetadataStorageInfoTool$: search_metadata.staging.try1.record_count 0 1510954401

Performance Testing

The perf_test tool creates files in a bucket with unique random metadata and queries Clueso in loop until the results for that file arrive.

It assumes the docker stack is running (via docker-compose or docker swarm).

This evaluates both query speed and latency, which may vary depending if cache is enabled and depending on cache_expiry in Clueso configuration.

Results can be published to Graphite and can be visualized using Grafana.

To run the performance tests against a local S3, run:

S3_ENDPOINT_URL="http://127.0.0.1" ./bin/perf_test.py 1

By default metrics are published to a local Graphite, assuming it's listening on port 2003. To override, run:

CARBON_SERVER="graphite-server-hostname" CARBON_PORT=2003 S3_ENDPOINT_URL="http://127.0.0.1" ./bin/perf_test.py 1

Configuration

In Livy, Spark settings can be set in spark-defaults.conf file, which is available in this repo on ./docker/images/clueso-docker-livy/conf/spark-defaults.conf.