Jar usage

The Qualification tool can be run in a three other ways if you are not using the CLI tool. One is to run it as a standalone tool on the Spark event logs after the application(s) have run, the second is to be integrated into a running Spark application using explicit API calls, and the third is to install a Spark listener which can output results on a per SQL query basis.

Prerequisites

  • Java 8 or above, Spark 3.0.1+ jars.

  • Spark event log(s) from Spark 2.0 or above version. Supports both rolled and compressed event logs with .lz4, .lzf, .snappy and .zstd suffixes as well as Databricks-specific rolled and compressed(.gz) event logs.

  • The tool does not support nested directories. Event log files or event log directories should be at the top level when specifying a directory.

Note

Spark event logs can be downloaded from Spark UI using a “Download” button on the right side, or can be found in the location specified by spark.eventLog.dir. See the Apache Spark Monitoring documentation for more information.

Step 01: Download the tools jar and Apache Spark 3 Distribution

The Qualification tool require the Spark 3.x jars to be able to run but do not need an Apache Spark run time. If you do not already have Spark 3.x installed, you can download the Spark distribution to any machine and include the jars in the classpath. - Download the latest jar from Maven repository

Step 02: Run the Qualification tool

  1. The Qualification tool reads the log files and process them in-memory. So the heap memory should be increased when processing large volume of events. It is recommended to pass VM options -Xmx10g and adjust according to the number-of-apps / size-of-logs being processed.

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    export QUALIFICATION_HEAP=-Xmx10g

  2. Event logs stored on a local machine:

    • Extract the Spark distribution into a local directory if necessary.

    • Either set SPARK_HOME to point to that directory or just put the path inside of the classpath java -cp toolsJar:pathToSparkJars/*:... when you run the Qualification tool.

    This tool parses the Spark CPU event log(s) and creates an output report. Acceptable inputs are either individual or multiple event logs files or directories containing spark event logs in the local filesystem, HDFS, S3 or mixed.

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    Usage: java ${QUALIFICATION_HEAP} \ -cp rapids-4-spark-tools_2.12-<version>.jar:$SPARK_HOME/jars/* \ com.nvidia.spark.rapids.tool.qualification.QualificationMain [options] <eventlogs | eventlog directories ...>

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    Sample: java ${QUALIFICATION_HEAP} \ -cp rapids-4-spark-tools_2.12-<version>.jar:$SPARK_HOME/jars/* \ com.nvidia.spark.rapids.tool.qualification.QualificationMain /usr/logs/app-name1

  3. Event logs stored on an on-premises HDFS cluster:

    Example running on files in HDFS: (include $HADOOP_CONF_DIR in classpath)

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    Usage: java ${QUALIFICATION_HEAP} \ -cp ~/rapids-4-spark-tools_2.12-<version>.jar:$SPARK_HOME/jars/*:$HADOOP_CONF_DIR/ \ com.nvidia.spark.rapids.tool.qualification.QualificationMain /eventlogDir

    Note, on an HDFS cluster, the default filesystem is likely HDFS for both the input and output so if you want to point to the local filesystem be sure to include file: in the path.

Qualification tool options

Note

--help should be before the trailing event logs.

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java -cp ~/rapids-4-spark-tools_2.12-<version>.jar:$SPARK_HOME/jars/*:$HADOOP_CONF_DIR/ \ com.nvidia.spark.rapids.tool.qualification.QualificationMain --help RAPIDS Accelerator Qualification tool for Apache Spark Usage: java -cp rapids-4-spark-tools_2.12-<version>.jar:$SPARK_HOME/jars/* com.nvidia.spark.rapids.tool.qualification.QualificationMain [options] <eventlogs | eventlog directories ...> --all Apply multiple event log filtering criteria and process only logs for which all conditions are satisfied.Example: <Filter1> <Filter2> <Filter3> --all -> result is <Filter1> AND <Filter2> AND <Filter3>. Default is all=true --any Apply multiple event log filtering criteria and process only logs for which any condition is satisfied.Example: <Filter1> <Filter2> <Filter3> --any -> result is <Filter1> OR <Filter2> OR <Filter3> -a, --application-name <arg> Filter event logs by application name. The string specified can be a regular expression, substring, or exact match. For filtering based on complement of application name, use ~APPLICATION_NAME. i.e Select all event logs except the ones which have application name as the input string. -f, --filter-criteria <arg> Filter newest or oldest N eventlogs based on application start timestamp, unique application name or filesystem timestamp. Filesystem based filtering happens before any application based filtering.For application based filtering, the order in which filters areapplied is: application-name, start-app-time, filter-criteria.Application based filter-criteria are:100-newest (for processing newest 100 event logs based on timestamp insidethe eventlog) i.e application start time) 100-oldest (for processing oldest 100 event logs based on timestamp insidethe eventlog) i.e application start time) 100-newest-per-app-name (select at most 100 newest log files for each unique application name) 100-oldest-per-app-name (select at most 100 oldest log files for each unique application name)Filesystem based filter criteria are:100-newest-filesystem (for processing newest 100 event logs based on filesystem timestamp). 100-oldest-filesystem (for processing oldest 100 event logsbased on filesystem timestamp). -h, --html-report Default is to generate an HTML report. --no-html-report Disables generating the HTML report. -m, --match-event-logs <arg> Filter event logs whose filenames contain the input string. Filesystem based filtering happens before any application based filtering. --max-sql-desc-length <arg> Maximum length of the SQL description string output with the per sql output. Default is 100. --ml-functions Report if there are any SparkML or Spark XGBoost functions in the eventlog. -n, --num-output-rows <arg> Number of output rows in the summary report. Default is 1000. --num-threads <arg> Number of thread to use for parallel processing. The default is the number of cores on host divided by 4. --order <arg> Specify the sort order of the report. desc or asc, desc is the default. desc (descending) would report applications most likely to be accelerated at the top and asc (ascending) would show the least likely to be accelerated at the top. -o, --output-directory <arg> Base output directory. Default is current directory for the default filesystem. The final output will go into a subdirectory called rapids_4_spark_qualification_output. It will overwrite any existing directory with the same name. -p, --per-sql Report at the individual SQL query level. --platform <arg> Cluster platform where Spark CPU workloads were executed. Options include onprem, dataproc-t4, dataproc-l4, emr, databricks-aws, and databricks-azure. Default is onprem. -r, --report-read-schema Whether to output the read formats and datatypes to the CSV file. This can be very long. Default is false. --spark-property <arg>... Filter applications based on certain Spark properties that were set during launch of the application. It can filter based on key:value pair or just based on keys. Multiple configs can be provided where the filtering is done if any of theconfig is present in the eventlog. filter on specific configuration: --spark-property=spark.eventLog.enabled:truefilter all eventlogs which has config: --spark-property=spark.driver.portMultiple configs: --spark-property=spark.eventLog.enabled:true --spark-property=spark.driver.port -s, --start-app-time <arg> Filter event logs whose application start occurred within the past specified time period. Valid time periods are min(minute),h(hours),d(days),w(weeks),m(months). If a period is not specified it defaults to days. -t, --timeout <arg> Maximum time in seconds to wait for the event logs to be processed. Default is 24 hours (86400 seconds) and must be greater than 3 seconds. If it times out, it will report what it was able to process up until the timeout. -u, --user-name <arg> Applications which a particular user has submitted. --help Show help message trailing arguments: eventlog (required) Event log filenames(space separated) or directories containing event logs. eg: s3a://<BUCKET>/eventlog1 /path/to/eventlog2

Example commands:

  • Process the 10 newest logs, and only output the top 3 in the output:

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java ${QUALIFICATION_HEAP} \ -cp ~/rapids-4-spark-tools_2.12-<version>.jar:$SPARK_HOME/jars/*:$HADOOP_CONF_DIR/ \ com.nvidia.spark.rapids.tool.qualification.QualificationMain -f 10-newest -n 3 /eventlogDir

  • Process last 100 days’ logs:

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java ${QUALIFICATION_HEAP} \ -cp ~/rapids-4-spark-tools_2.12-<version>.jar:$SPARK_HOME/jars/*:$HADOOP_CONF_DIR/ \ com.nvidia.spark.rapids.tool.qualification.QualificationMain -s 100d /eventlogDir

  • Process only the newest log with the same application name:

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java ${QUALIFICATION_HEAP} \ -cp ~/rapids-4-spark-tools_2.12-<version>.jar:$SPARK_HOME/jars/*:$HADOOP_CONF_DIR/ \ com.nvidia.spark.rapids.tool.qualification.QualificationMain -f 1-newest-per-app-name /eventlogDir

  • Parse ML functions from the eventlog:

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java ${QUALIFICATION_HEAP} \ -cp ~/rapids-4-spark-tools_2.12-<version>.jar:$SPARK_HOME/jars/*:$HADOOP_CONF_DIR/ \ com.nvidia.spark.rapids.tool.qualification.QualificationMain --ml-functions /eventlogDir

Note

The “regular expression” used by -a option is based on java.util.regex.Pattern.

We provide a Spark Listener that can be installed at application start that will produce output for each SQL queries in the running application and indicate if that query is a good fit to try with the Rapids Accelerator for Spark.

Prerequisites

  • Java 8 or above, Spark 3.0.1+

Download the tools jar

Configuration

Add the RunningQualificationEventProcess to the spark listeners configuration: spark.extraListeners=org.apache.spark.sql.rapids.tool.qualification.RunningQualificationEventProcessor

The user should specify the output directory if they want the output to go to separate files, otherwise it will go to the Spark driver log. If the output directory is specified, it outputs two different files, one csv and one pretty printed log file. The output directory can be a local directory or point to a distributed file system or blobstore like S3.

  • spark.rapids.qualification.outputDir

By default, this will output results for 10 SQL queries per file and will keep 100 files. This behavior is because many blob stores don’t show files until they are fully written so you wouldn’t be able to see the results for a running application until it finishes the number of SQL queries per file. This behavior can be configured with the following configs.

  • spark.rapids.qualification.output.numSQLQueriesPerFile

    • default 10

  • spark.rapids.qualification.output.maxNumFiles

    • default 100

Run the Spark application

Run the application and include the tools jar, spark.extraListeners config and optionally the other configs to control the tools behavior.

For example:

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$SPARK_HOME/bin/spark-shell \ --jars rapids-4-spark-tools_2.12-<version>.jar \ --conf spark.extraListeners=org.apache.spark.sql.rapids.tool.qualification.RunningQualificationEventProcessor \ --conf spark.rapids.qualification.outputDir=/tmp/qualPerSqlOutput \ --conf spark.rapids.qualification.output.numSQLQueriesPerFile=5 \ --conf spark.rapids.qualification.output.maxNumFiles=10

After running some SQL queries you can look in the output directory and see files like:

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rapids_4_spark_qualification_output_persql_0.csv rapids_4_spark_qualification_output_persql_0.log rapids_4_spark_qualification_output_persql_1.csv rapids_4_spark_qualification_output_persql_1.log rapids_4_spark_qualification_output_persql_2.csv rapids_4_spark_qualification_output_persql_2.log

See the Understanding the Qualification tool output section on the file contents details.

Prerequisites

  • Java 8 or above, Spark 3.0.1+

Download the tools jar

Modify your application code to call the APIs

Currently only Scala api’s are supported. Note this does not support reporting at the per sql level currently. This can be done manually by just wrapping and reporting around those queries instead of the entire application.

Create the RunningQualicationApp:

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val qualApp = new com.nvidia.spark.rapids.tool.qualification.RunningQualificationApp()

Get the event listener from it and install it as a Spark listener:

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val listener = qualApp.getEventListener spark.sparkContext.addSparkListener(listener)

Run your queries and then get the summary or detailed output to see the results.

The summary output api:

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/** * Get the summary report for qualification. * @param delimiter The delimiter separating fields of the summary report. * @param prettyPrint Whether to including the separate at start and end and * add spacing so the data rows align with column headings. * @return String of containing the summary report. */ getSummary(delimiter: String = "|", prettyPrint: Boolean = true): String

The detailed output api:

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/** * Get the detailed report for qualification. * @param delimiter The delimiter separating fields of the summary report. * @param prettyPrint Whether to including the separate at start and end and * add spacing so the data rows align with column headings. * @return String of containing the detailed report. */ getDetailed(delimiter: String = "|", prettyPrint: Boolean = true, reportReadSchema: Boolean = false): String

Example:

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// run your sql queries ... // To get the summary output: val summaryOutput = qualApp.getSummary() // To get the detailed output: val detailedOutput = qualApp.getDetailed() // print the output somewhere for user to see println(summaryOutput) println(detailedOutput)

If you need to specify the tools jar as a maven dependency to compile the Spark application:

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<dependency> <groupId>com.nvidia</groupId> <artifactId>rapids-4-spark-tools_2.12</artifactId> <version>${version}</version> </dependency>

Run the Spark application

  • Run your Spark application and include the tools jar you downloaded with the spark ‘–jars’ options and view the output wherever you had it printed.

For example, if running the spark-shell:

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$SPARK_HOME/bin/spark-shell --jars rapids-4-spark-tools_2.12-<version>.jar

© Copyright 2023, NVIDIA. Last updated on Dec 20, 2023.