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new 9477e01 Add benchmark documentation. (#5683)
9477e01 is described below
commit 9477e014ed7e053e9d098b15742dbbdfe7e85493
Author: Nikola Grcevski <6207777+grcev...@users.noreply.github.com>
AuthorDate: Sat Jul 11 15:05:26 2020 -0400
Add benchmark documentation. (#5683)
---
contrib/pinot-druid-benchmark/README.md | 297 ++++++++++++++++++++++++++++++++
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+# Running the benchmark
+
+For instructions on how to run the Pinot/Druid benchmark please refer to the
+```run_benchmark.sh``` file.
+
+In order to run the Apache Pinot benchmark you'll need to create the
appropriate
+data segments, which are too large to be included in this github repository and
+they may need to be recreated with new Apache Pinot versions.
+
+To create the neccessary segment data for the benchmark please follow the
+instructions below.
+
+# Creating Apache Pinot benchmark segments from TPC-H data
+
+To run the Pinot/Druid benchmark with Apache Pinot you'll need to download and
run
+the TPC-H tools to generate the benchmark data sets.
+
+## Downloading and building the TPC-H tools
+
+The TPC-H tools can be downloaded from the [TPC-H
Website](http://www.tpc.org/tpch/default5.asp).
+Registration is required.
+
+**Note:**: The instructions below for dbgen assume a Linux OS.
+
+After downloading and extracing the TPC-H tools, you'll need to build the
+db generator tool: ```dbgen```. To do so, extract the package that you have
+downloaded from TPC-H's website and inside the dbgen sub directory edit the
+```makefile``` file.
+
+Set the following variables in the makefile to:
+
+```
+CC = gcc
+...
+DATABASE= SQLSERVER
+MACHINE = LINUX
+WORKLOAD = TPCH
+```
+
+Next, build the dbgen tool as per the README instructions in the dbgen
directory.
+
+## Generating the TPC-H data and converting them for use in Apache Pinot
+
+After building ```dbgen``` run the following command line in the ```dbgen```
directory:
+
+```
+./dbgen -TL -s8
+```
+
+The command above will generate a single large file called ```lineitem.tbl```.
+This is the data file for the TPC-H benchmark, which we'll need to
post-process
+a bit to be imported into Apache Pinot.
+
+Next, build the Pinot/Druid Benchmark code if you haven't done so already.
+
+**Note:** Apache Pinot has JDK11 support, however for now it's
+best to use JDK8 for all build and run operations in this manual.
+
+Inside ```pinot_directory/contrib/pinot-druid-benchmark``` run:
+
+```
+mvn clean install
+```
+
+Next, inside the same directory split the ```lineitem``` table:
+
+```
+./target/appassembler/bin/data-separator.sh <Path to lineitem.tbl> <Output
Directory>
+```
+
+Use the output directory from the split as the input directory for the merge
+command below:
+
+```
+./target/appassembler/bin/data-merger.sh <Input Directory> <Output Directory>
YEAR
+```
+
+If all ran well you should see a few CSV files produced, 1992.csv through
1998.csv.
+
+These files are the starting point for creating our Apache Pinot segments.
+
+## Create the Apache Pinot segments
+
+The first step in the process is to launch a standalone Apache Pinot Cluster
on one
+single server. This cluster will serve as a host to hold the initial segments,
+which we'll extract and copy for later re-use in the benchmark.
+
+Follow the steps outlined in the Apache Pinot Manual Cluster setup to launch
the
+cluster:
+
+https://docs.pinot.apache.org/basics/getting-started/advanced-pinot-setup
+
+You don't need the Kafka service as we won't be using it.
+
+Next, we need to follow the instructions similar to the ones described in
+the [Batch Import
Example](https://docs.pinot.apache.org/basics/getting-started/pushing-your-data-to-pinot)
+in the Apache Pinot documentation.
+
+### Create the Apache Pinot tables
+
+Run:
+
+```
+pinot-admin.sh AddTable \
+ -tableConfigFile /absolute/path/to/table-config.json \
+ -schemaFile /absolute/path/to/schema.json -exec
+```
+
+For this command above you'll need the following configuration files:
+
+```table_config.json```
+```
+{
+ "tableName": "tpch_lineitem",
+ "segmentsConfig" : {
+ "replication" : "1",
+ "schemaName" : "tpch_lineitem",
+ "segmentAssignmentStrategy" : "BalanceNumSegmentAssignmentStrategy"
+ },
+ "tenants" : {
+ "broker":"DefaultTenant",
+ "server":"DefaultTenant"
+ },
+ "tableIndexConfig" : {
+ "starTreeIndexConfigs":[{
+ "maxLeafRecords": 100,
+ "functionColumnPairs": ["SUM__l_extendedprice", "SUM__l_discount",
"SUM__l_quantity"],
+ "dimensionsSplitOrder": ["l_receiptdate", "l_shipdate", "l_shipmode",
"l_returnflag"],
+ "skipStarNodeCreationForDimensions": [],
+ "skipMaterializationForDimensions": ["l_partkey", "l_commitdate",
"l_linestatus", "l_comment", "l_orderkey", "l_shipinstruct", "l_linenumber",
"l_suppkey"]
+ }]
+ },
+ "tableType":"OFFLINE",
+ "metadata": {}
+}
+```
+
+```schema.json```
+```
+{
+ "schemaName": "tpch_lineitem",
+ "dimensionFieldSpecs": [
+ {
+ "name": "l_orderkey",
+ "dataType": "INT"
+ },
+ {
+ "name": "l_partkey",
+ "dataType": "INT"
+ },
+ {
+ "name": "l_suppkey",
+ "dataType": "INT"
+ },
+ {
+ "name": "l_linenumber",
+ "dataType": "INT"
+ },
+ {
+ "name": "l_returnflag",
+ "dataType": "STRING"
+ },
+ {
+ "name": "l_linestatus",
+ "dataType": "STRING"
+ },
+ {
+ "name": "l_shipdate",
+ "dataType": "STRING"
+ },
+ {
+ "name": "l_commitdate",
+ "dataType": "STRING"
+ },
+ {
+ "name": "l_receiptdate",
+ "dataType": "STRING"
+ },
+ {
+ "name": "l_shipinstruct",
+ "dataType": "STRING"
+ },
+ {
+ "name": "l_shipmode",
+ "dataType": "STRING"
+ },
+ {
+ "name": "l_comment",
+ "dataType": "STRING"
+ }
+ ],
+ "metricFieldSpecs": [
+ {
+ "name": "l_quantity",
+ "dataType": "LONG"
+ },
+ {
+ "name": "l_extendedprice",
+ "dataType": "DOUBLE"
+ },
+ {
+ "name": "l_discount",
+ "dataType": "DOUBLE"
+ },
+ {
+ "name": "l_tax",
+ "dataType": "DOUBLE"
+ }
+ ]
+}
+```
+
+**Note:** The configuration as specified above will give you
+the data with the **optimal star tree index**. The index configuration is
+specified in the ```tableIndexConfig``` section in the ```table_config.json```
file. If
+you want to generate a different type of indexed segment, then you
+should modify the tableIndexConfig section to reflect the correct index
+type as described in the [Indexing
Section](https://docs.pinot.apache.org/basics/features/indexing)
+of the Apache Pinot Documentation.
+
+### Create the Apache Pinot segments
+
+Next, we'll create the segments for this Apache Pinot table using the optimal
+star tree index configuration.
+
+For this purpose you'll need a job specification YAML file. Here's an example
+that does the TPC-H data import:
+
+```job-spec.yml```
+```
+executionFrameworkSpec:
+ name: 'standalone'
+ segmentGenerationJobRunnerClassName:
'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner'
+ segmentTarPushJobRunnerClassName:
'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentTarPushJobRunner'
+ segmentUriPushJobRunnerClassName:
'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentUriPushJobRunner'
+jobType: SegmentCreationAndTarPush
+inputDirURI:
'/absolute/path/to/incubator-pinot/contrib/pinot-druid-benchmark/data_out/raw_data/'
+includeFileNamePattern: 'glob:**/*.csv'
+outputDirURI:
'/absolute/path/to/incubator-pinot/contrib/pinot-druid-benchmark/data_out/segments/'
+overwriteOutput: true
+pinotFSSpecs:
+ - scheme: file
+ className: org.apache.pinot.spi.filesystem.LocalPinotFS
+recordReaderSpec:
+ dataFormat: 'csv'
+ className: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReader'
+ configClassName:
'org.apache.pinot.plugin.inputformat.csv.CSVRecordReaderConfig'
+ configs:
+ delimiter: '|'
+ header:
'l_orderkey|l_partkey|l_suppkey|l_linenumber|l_quantity|l_extendedprice|l_discount|l_tax|l_returnflag|l_linestatus|l_shipdate|l_commitdate|l_receiptdate|l_shipinstruct|l_shipmode|l_comment|'
+tableSpec:
+ tableName: 'tpch_lineitem'
+ schemaURI: 'http://localhost:9000/tables/tpch_lineitem/schema'
+ tableConfigURI: 'http://localhost:9000/tables/tpch_lineitem'
+pinotClusterSpecs:
+ - controllerURI: 'http://localhost:9000'
+```
+
+**Note:** Make sure you modify the absolute path for **inputDirURI** and
**outputDirURI**
+above. The inputDirURI should be pointing to the directory where you have
+generated the 7 YEAR CSV files, 1992.csv through 1998.csv.
+
+After you have modified the input and output dir, run the job as described in
the
+[Batch Import
Example](https://docs.pinot.apache.org/basics/getting-started/pushing-your-data-to-pinot)
document:
+
+
+```
+pinot-admin.sh LaunchDataIngestionJob \
+ -jobSpecFile /absolute/path/to/job-spec.yml
+```
+
+The segment creation output on the console will tell you where Apache Pinot
will
+store the created segments (it should be your output dir). You should see a
+line appear in the output as:
+
+```
+...
+outputDirURI:
/absolute/path/to/incubator-pinot/contrib/pinot-druid-benchmark/data_out/segments/
+...
+```
+
+Inside there you'll find the tpch_lineitem_OFFLINE directory with 7 separate
+segments, 0 through 6. Tar/GZip the whole directory and this will be your
+optimal_startree_small_yearly temp segment that the benchmark requires.
However,
+wait first for the segment creation to finish.
+
+Try few queries to ensure that the segments are working. You can find some
+sample queries under the benchmark directory
```src/main/resources/pinot_queries```.
+Watch the console output from the Apache Pinot cluster as you run the queries,
and make sure
+there are no complaints in there that the queries were slow since index wasn't
found.
+If you see a message saying the query was slow, it means that the indexes
weren't
+created properly. With the optimal star tree index your total query time
should be
+few milliseconds at most.
+
+You can now shutdown the Apache Pinot cluster which you started manually and
when you
+launch the benchmark server cluster it will pick up your new segments.
+
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