cb501771fa
### What changes were proposed in this pull request? This PR aims the followings. - Refactor `TPCDSQueryBenchmark` to use main method to improve the usability. - Reduce the number of iteration from 5 to 2 because it takes too long. (2 is okay because we have `Stdev` field now. If there is an irregular run, we can notice easily with that). - Generate one result file for TPCDS scale factor 1. (Note that this test suite can be used for the other scale factors, too.) - AWS EC2 `r3.xlarge` with `ami-06f2f779464715dc5 (ubuntu-bionic-18.04-amd64-server-20190722.1)` is used. This PR adds a JDK8 result based on the TPCDS ScaleFactor 1G data generated by the following. ``` # `spark-tpcds-datagen` needs this. (JDK8) $ git clone https://github.com/apache/spark.git -b branch-2.4 --depth 1 spark-2.4 $ export SPARK_HOME=$PWD $ ./build/mvn clean package -DskipTests # Generate data. (JDK8) $ git clone gitgithub.com:maropu/spark-tpcds-datagen.git $ cd spark-tpcds-datagen/ $ build/mvn clean package $ mkdir -p /data/tpcds $ ./bin/dsdgen --output-location /data/tpcds/s1 // This need `Spark 2.4` ``` ### Why are the changes needed? Although the generated TPCDS data is random, we can keep the record. ### Does this PR introduce any user-facing change? No. (This is dev-only test benchmark). ### How was this patch tested? Manually run the benchmark. Please note that you need to have TPCDS data. ``` SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.TPCDSQueryBenchmark --data-location /data/tpcds/s1" ``` Closes #26049 from dongjoon-hyun/SPARK-25668. Authored-by: Dongjoon Hyun <dhyun@apple.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
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catalyst | ||
core | ||
hive | ||
hive-thriftserver | ||
create-docs.sh | ||
gen-sql-markdown.py | ||
mkdocs.yml | ||
README.md |
Spark SQL
This module provides support for executing relational queries expressed in either SQL or the DataFrame/Dataset API.
Spark SQL is broken up into four subprojects:
- Catalyst (sql/catalyst) - An implementation-agnostic framework for manipulating trees of relational operators and expressions.
- Execution (sql/core) - A query planner / execution engine for translating Catalyst's logical query plans into Spark RDDs. This component also includes a new public interface, SQLContext, that allows users to execute SQL or LINQ statements against existing RDDs and Parquet files.
- Hive Support (sql/hive) - Includes extensions that allow users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes. There are also wrappers that allow users to run queries that include Hive UDFs, UDAFs, and UDTFs.
- HiveServer and CLI support (sql/hive-thriftserver) - Includes support for the SQL CLI (bin/spark-sql) and a HiveServer2 (for JDBC/ODBC) compatible server.
Running ./sql/create-docs.sh
generates SQL documentation for built-in functions under sql/site
.