d65f534c5a
### What changes were proposed in this pull request? With benchmark original, where the timestamp values are valid to the new parser the result is ```scala [info] Running benchmark: Read dates and timestamps [info] Running case: timestamp strings [info] Stopped after 3 iterations, 5781 ms [info] Running case: parse timestamps from Dataset[String] [info] Stopped after 3 iterations, 44764 ms [info] Running case: infer timestamps from Dataset[String] [info] Stopped after 3 iterations, 93764 ms [info] Running case: from_json(timestamp) [info] Stopped after 3 iterations, 59021 ms ``` When we modify the benchmark to ```scala def timestampStr: Dataset[String] = { spark.range(0, rowsNum, 1, 1).mapPartitions { iter => iter.map(i => s"""{"timestamp":"1970-01-01T01:02:03.${i % 100}"}""") }.select($"value".as("timestamp")).as[String] } readBench.addCase("timestamp strings", numIters) { _ => timestampStr.noop() } readBench.addCase("parse timestamps from Dataset[String]", numIters) { _ => spark.read.schema(tsSchema).json(timestampStr).noop() } readBench.addCase("infer timestamps from Dataset[String]", numIters) { _ => spark.read.json(timestampStr).noop() } ``` where the timestamp values are invalid for the new parser which causes a fallback to legacy parser(2.4). the result is ```scala [info] Running benchmark: Read dates and timestamps [info] Running case: timestamp strings [info] Stopped after 3 iterations, 5623 ms [info] Running case: parse timestamps from Dataset[String] [info] Stopped after 3 iterations, 506637 ms [info] Running case: infer timestamps from Dataset[String] [info] Stopped after 3 iterations, 509076 ms ``` About 10x perf-regression BUT if we modify the timestamp pattern to `....HH:mm:ss[.SSS][XXX]` which make all timestamp values valid for the new parser to prohibit fallback, the result is ```scala [info] Running benchmark: Read dates and timestamps [info] Running case: timestamp strings [info] Stopped after 3 iterations, 5623 ms [info] Running case: parse timestamps from Dataset[String] [info] Stopped after 3 iterations, 506637 ms [info] Running case: infer timestamps from Dataset[String] [info] Stopped after 3 iterations, 509076 ms ``` ### Why are the changes needed? Fix performance regression. ### Does this PR introduce any user-facing change? NO ### How was this patch tested? new tests added. Closes #28181 from yaooqinn/SPARK-31414. Authored-by: Kent Yao <yaooqinn@hotmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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.. | ||
catalyst | ||
core | ||
hive | ||
hive-thriftserver | ||
create-docs.sh | ||
gen-sql-api-docs.py | ||
gen-sql-config-docs.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
, and SQL configuration documentation that gets included as part of configuration.md
in the main docs
directory.