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MapD Benchmark Script

Python scripts that leverages pymapd to query a mapd database and reports performance of queries or data import.

Setup

Scripts are intended for use on Python3

All required python packages can be installed with pip using the requirements.txt file: ``` pip install -r requirements.txt ```

The required packages can otherwise be installed individually using conda or pip. List of required packages:

1) pymapd - Provides a python DB API 2.0-compliant OmniSci interface (formerly MapD)

Install with conda: conda install -c conda-forge pymapd or with pip: pip install pymapd

2) pandas - Provides high-performance, easy-to-use data structures and data analysis tools

Install with conda: conda install pandas or with pip: pip install pandas

3) numpy - Package for scientific computing with Python

Install with conda: conda install -c anaconda numpy or with pip: pip install numpy

4) nvidia-ml-py3 - Python bindings to the NVIDIA Management Library

Install with conda: conda install -c fastai nvidia-ml-py3 or with pip: pip install nvidia-ml-py3

Usage

Required components

Running the script requies a few components:

1) Connection to a mapd db with a dataset loaded - either a large sample dataset used for benchmarking, or a custom dataset. 2) Query(ies) to run against the dataset. These can be provided as files with the following syntax: query_<query_id>.sql (where query_id is a unique query ID). The script will find all queries from files that match the syntax in the directory passed in to the script at runtime. 3) Destination. Depending on the type of destination, a connection to a mapd db, or destination file location may be required.

Running the script

Usage can be printed at any time by running: ./run-benchmark.py -h or --help

Currently, usage is:

``` usage: run-benchmark.py [-h] [-v] [-q] [-u USER] [-p PASSWD] [-s SERVER] [-o PORT] [-n NAME] -t TABLE -l LABEL [-d QUERIES_DIR] -i ITERATIONS [-g GPU_COUNT] [-G GPU_NAME] [–no-gather-conn-gpu-info] [–no-gather-nvml-gpu-info] [–gather-nvml-gpu-info] [-m MACHINE_NAME] [-a MACHINE_UNAME] [-e DESTINATION] [-U DEST_USER] [-P DEST_PASSWD] [-S DEST_SERVER] [-O DEST_PORT] [-N DEST_NAME] [-T DEST_TABLE] [-C DEST_TABLE_SCHEMA_FILE] [-j OUTPUT_FILE_JSON] [-J OUTPUT_FILE_JENKINS] [-E OUTPUT_TAG_JENKINS]

required arguments: -u USER, –user USER Source database user -p PASSWD, –passwd PASSWD Source database password -s SERVER, –server SERVER Source database server hostname -n NAME, –name NAME Source database name -t TABLE, –table TABLE Source db table name -l LABEL, –label LABEL Benchmark run label -d QUERIES_DIR, –queries-dir QUERIES_DIR Absolute path to dir with query files. [Default: "queries" dir in same location as script] -i ITERATIONS, –iterations ITERATIONS Number of iterations per query. Must be > 1

optional arguments: -h, –help show this help message and exit -v, –verbose Turn on debug logging -q, –quiet Suppress script outuput (except warnings and errors) -o PORT, –port PORT Source database server port -g GPU_COUNT, –gpu-count GPU_COUNT Number of GPUs. Not required when gathering local gpu info -G GPU_NAME, –gpu-name GPU_NAME Name of GPU(s). Not required when gathering local gpu info –no-gather-conn-gpu-info Do not gather source database GPU info fields [run_gpu_count, run_gpu_mem_mb] using pymapd connection info. Use when testing a CPU-only server. –no-gather-nvml-gpu-info Do not gather source database GPU info fields [gpu_driver_ver, run_gpu_name] from local GPU using pynvml. Defaults to True when source server is not "localhost". Use when testing a CPU-only server. –gather-nvml-gpu-info Gather source database GPU info fields [gpu_driver_ver, run_gpu_name] from local GPU using pynvml. Defaults to True when source server is "localhost". Only use when benchmarking against same machine that this script is run from. -m MACHINE_NAME, –machine-name MACHINE_NAME Name of source machine -a MACHINE_UNAME, –machine-uname MACHINE_UNAME Uname info from source machine -e DESTINATION, –destination DESTINATION Destination type: [mapd_db, file_json, output, jenkins_bench] Multiple values can be input seperated by commas, ex: "mapd_db,file_json" -U DEST_USER, –dest-user DEST_USER Destination mapd_db database user -P DEST_PASSWD, –dest-passwd DEST_PASSWD Destination mapd_db database password -S DEST_SERVER, –dest-server DEST_SERVER Destination mapd_db database server hostname (required if destination = "mapd_db") -O DEST_PORT, –dest-port DEST_PORT Destination mapd_db database server port -N DEST_NAME, –dest-name DEST_NAME Destination mapd_db database name -T DEST_TABLE, –dest-table DEST_TABLE Destination mapd_db table name -C DEST_TABLE_SCHEMA_FILE, –dest-table-schema-file DEST_TABLE_SCHEMA_FILE Destination table schema file. This must be an executable CREATE TABLE statement that matches the output of this script. It is required when creating the results table. Default location is in "./results_table_schemas/query-results.sql" -j OUTPUT_FILE_JSON, –output-file-json OUTPUT_FILE_JSON Absolute path of .json output file (required if destination = "file_json") -J OUTPUT_FILE_JENKINS, –output-file-jenkins OUTPUT_FILE_JENKINS Absolute path of jenkins benchmark .json output file (required if destination = "jenkins_bench") -E OUTPUT_TAG_JENKINS, –output-tag-jenkins OUTPUT_TAG_JENKINS Jenkins benchmark result tag. Optional, appended to table name in "group" field ```

Example 1: ``` python ./run-benchmark.py -t flights_2008_10k -l TestLabel -d /data/queries/flights -i 10 -g 4 -S localhost ``` this would run the script with the following parameters:

  • Default values for source mapd db: localhost, default username and password, and database name
  • Queries would be run against the "flights_2008_10k" table
  • Results would have label "TestLabel"
  • Query file(s) would be sources from directory "/data/queries/flights"
  • Query(ies) would run for 10 iterations each
  • Results would show that mapd_db machine has 4 GPUs
  • Destination mapd db is located at localhost with the default username, password, and database name.

Example 2: ``` python ./run-benchmark.py -u user -p password -s mapd-server.example.com -n mapd_db -t flights_2008_10k -l TestLabel -d /home/mapd/queries/flights -i 10 -g 4 -e mapd_db,file_json -U dest_user -P password -S mapd-dest-server.mapd.com -N mapd_dest_db -T benchmark_results -j /home/mapd/benchmark_results/example.json ```

Data sources

Current list of data sources and availability:

DatasetSource
Flightshttps://omnisci-benchmark-data-us-west-2.s3-us-west-2.amazonaws.com/flights.csv.tar.gz
Taxishttps://tech.marksblogg.com/billion-nyc-taxi-rides-redshift.html
TPC-Hhttp://www.tpc.org/tpch/

Comparing results

The python script analyze-benchmark.py can be used to compare results between benchmark runs. To use the script, results for each benchmark run must be stored in .json format using file_json DESTINATION. Two directories can be passed to the script: "sample" (-s) containing .json test results from the base sample set; and another, "reference" (-r) with the results to use for comparison.

Example usage: ``` python analyze-benchmark.py -s ./results/base_test -r ./results/example_test1 `` where./results/base_testand./results/example_test1directories contain the .json output files from therun-benchmark.py` script for their respective test runs.

Import Benchmark

The import benchmark script ./run-benchmark-import.py is used to run a data import from a file local to the benchmarking machine, and report various times associated with the import of that data.

Usage

Required components

Running the script requies a few components:

1) Connection to a db, can be local or remote. 2) Data source file(s) on the machine with the database. 3) Schemas associates with each data source file to create the table before performing the import. These must be executable .sql files kept in the ./import_table_schemas directory. 3) Destination for the results. Depending on the type of destination, a connection to a mapd db, or destination file location may be required.

Running the script

Usage can be printed at any time by running: ./run-benchmark-import.py -h or --help

Currently, usage is:

``` usage: run-benchmark-import.py [-h] [-v] [-q] [-u USER] [-p PASSWD] [-s SERVER] [-o PORT] [-n NAME] -l LABEL -f IMPORT_FILE -c TABLE_SCHEMA_FILE [-t IMPORT_TABLE_NAME] [-F IMPORT_QUERY_TEMPLATE_FILE] [–no-drop-table-before] [–no-drop-table-after] [-A IMPORT_TEST_NAME] [-m MACHINE_NAME] [-a MACHINE_UNAME] [-e DESTINATION] [-U DEST_USER] [-P DEST_PASSWD] [-S DEST_SERVER] [-O DEST_PORT] [-N DEST_NAME] [-T DEST_TABLE] [-C DEST_TABLE_SCHEMA_FILE] [-j OUTPUT_FILE_JSON] [-J OUTPUT_FILE_JENKINS]

required arguments: -u USER, –user USER Source database user -p PASSWD, –passwd PASSWD Source database password -s SERVER, –server SERVER Source database server hostname -n NAME, –name NAME Source database name -l LABEL, –label LABEL Benchmark run label -f IMPORT_FILE, –import-file IMPORT_FILE Absolute path to file on omnisci_server machine with data for import test -c TABLE_SCHEMA_FILE, –table-schema-file TABLE_SCHEMA_FILE Path to local file with CREATE TABLE sql statement for the import table

optional arguments: -h, –help show this help message and exit -v, –verbose Turn on debug logging -q, –quiet Suppress script outuput (except warnings and errors) -o PORT, –port PORT Source database server port -t IMPORT_TABLE_NAME, –import-table-name IMPORT_TABLE_NAME Name of table to import data to. NOTE: This table will be dropped before and after the import test, unless –no-drop-table-[before/after] is specified. -F IMPORT_QUERY_TEMPLATE_FILE, –import-query-template-file IMPORT_QUERY_TEMPLATE_FILE Path to file containing template for import query. The script will replace "##TAB##" with the value of import_table_name and "##FILE##" with the value of table_schema_file. By default, the script will use the COPY FROM command with the default default delimiter (,). –no-drop-table-before Do not drop the import table and recreate it before import NOTE: Make sure existing table schema matches import .csv file schema –no-drop-table-after Do not drop the import table after import -A IMPORT_TEST_NAME, –import-test-name IMPORT_TEST_NAME Name of import test (ex: "ips"). Required when using jenkins_bench_json as output. -m MACHINE_NAME, –machine-name MACHINE_NAME Name of source machine -a MACHINE_UNAME, –machine-uname MACHINE_UNAME Uname info from source machine -e DESTINATION, –destination DESTINATION Destination type: [mapd_db, file_json, output, jenkins_bench] Multiple values can be input seperated by commas, ex: "mapd_db,file_json" -U DEST_USER, –dest-user DEST_USER Destination mapd_db database user -P DEST_PASSWD, –dest-passwd DEST_PASSWD Destination mapd_db database password -S DEST_SERVER, –dest-server DEST_SERVER Destination mapd_db database server hostname (required if destination = "mapd_db") -O DEST_PORT, –dest-port DEST_PORT Destination mapd_db database server port -N DEST_NAME, –dest-name DEST_NAME Destination mapd_db database name -T DEST_TABLE, –dest-table DEST_TABLE Destination mapd_db table name -C DEST_TABLE_SCHEMA_FILE, –dest-table-schema-file DEST_TABLE_SCHEMA_FILE Destination table schema file. This must be an executable CREATE TABLE statement that matches the output of this script. It is required when creating the results table. Default location is in "./results_table_schemas/query-results.sql" -j OUTPUT_FILE_JSON, –output-file-json OUTPUT_FILE_JSON Absolute path of .json output file (required if destination = "file_json") -J OUTPUT_FILE_JENKINS, –output-file-jenkins OUTPUT_FILE_JENKINS Absolute path of jenkins benchmark .json output file (required if destination = "jenkins_bench") ```

Synthetic benchmark queries

The goal of this benchmark is mainly to enable developers to measure performance variations in certain controlled scenarios. Secondary, by increasing the query coverage, it can be used to track down unintentional performance regressions.

How to use

To use it, the user should run an omnisci_server. Then, the benchmark process can be started by using the run_synthetic_benchmark.py script. Since this code uses the same code structure provided by run_benchmark.py, then it needs the same set of python libraries installed and used as well (e.g., pip install requirements.txt). ``` cd Benchmarks

python3 run_synthetic_benchmark.py –label MasterTest –gpu_label 2080ti –gpu_count 2 –iterations 10 –num_fragments 4 –fragment_size 32000000 –query all –print_results ```

The results for each query group is shown in the terminal until all are finished. Also, the results are structured in a way that analyze_benchmark.py code can be used to compare two different runs together.

If data is already created and imported into the database, then data generation and following verification and potential data import can all be avoided by using --skip_data_gen_and_import. By doing so, the script goes directly into running all requested query groups.

Creating synthetic data

The current code generates a set of randomly generated entries based on the pre-defined schema, all being decided at synthetic_benchmark/create_table.py. By default, when the run_synthetic_benchmark.py script is run, it is first checked whether such table with expected schema and expected number of rows already exist in the database or not. If yes, it proceeds to the next step which actually runs the queries. If no, at first we generate proper data (in csv format), and then create a table in the database. Finally we import the generated data into the new table so that benchmark queries can be run on it.

It is also possible to use the code to just generate random data, without any subsequent data import and benchmarking. To do so, it is possible to directly apply the create_table.py's main module:

``` python3 synthetic_benchmark/create_table.py –data_dir /data/dir/to/store/data –num_fragments 4 –fragment_size 32000000 –just_data_generation ```

It is also possible to import a previously created data, without going through the benchmark structure and directly through the create_table.py: ``` python3 synthetic_benchmark/create_table.py –data_dir /data/dir/to/store/data –table_name bench_test –num_fragments 4 –fragment_size 32000000 –just_data_import ```

Running different queries in various groups:

After verifying that proper data is already available in the database, benchmark queries are run using the run_benchmark.py script. Queries are categorized into several query groups (e.g., BaselineHash, PPerfectHashSingleCol, Sort, etc.). By default, if no option is chosen for --query argument, then all query groups are run and results are stored.

Additional details

1) Import query template file: If the import command needs to be customized - for example, to use a delimiter other than comma - an import query template file can be used. This file must contain an executable query with two variables that will be replaced by the script: a) ##TAB## will be replaced with the import table name, and b) ##FILE## will be replaced with the import data file.