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Eric Wu 15df2a3f40 [SPARK-31079][SQL] Logging QueryExecutionMetering in RuleExecutor logger 
### What changes were proposed in this pull request?
RuleExecutor already support metering for analyzer/optimizer rules. By providing such information in `PlanChangeLogger`, user can get more information when debugging rule changes .

This PR enhanced `PlanChangeLogger` to display RuleExecutor metrics. This can be easily done by calling the existing API `resetMetrics` and `dumpTimeSpent`, but there might be conflicts if user is also collecting total metrics of a sql job. Thus I introduced `QueryExecutionMetrics`, as the snapshot of `QueryExecutionMetering`, to better support this feature.

Information added to `PlanChangeLogger`
```
=== Metrics of Executed Rules ===
Total number of runs: 554
Total time: 0.107756568 seconds
Total number of effective runs: 11
Total time of effective runs: 0.047615486 seconds
```

### Why are the changes needed?
Provide better plan change debugging user experience

### Does this PR introduce any user-facing change?
Only add more debugging info of `planChangeLog`, default log level is TRACE.

### How was this patch tested?
Update existing tests to verify the new logs

Closes #27846 from Eric5553/ExplainRuleExecMetrics.

Authored-by: Eric Wu <492960551@qq.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
2020-03-10 19:08:59 +08:00
..
catalyst [SPARK-31079][SQL] Logging QueryExecutionMetering in RuleExecutor logger 2020-03-10 19:08:59 +08:00
core [SPARK-31065][SQL] Match schema_of_json to the schema inference of JSON data source 2020-03-10 00:33:32 -07:00
hive [SPARK-31061][SQL] Provide ability to alter the provider of a table 2020-03-05 23:42:07 -08:00
hive-thriftserver [SPARK-30049][SQL] SQL fails to parse when comment contains an unmatched quote character 2020-03-03 09:55:15 -06:00
create-docs.sh [SPARK-30510][SQL][DOCS] Publicly document Spark SQL configuration options 2020-02-09 19:20:47 +09:00
gen-sql-api-docs.py [SPARK-30510][SQL][DOCS] Publicly document Spark SQL configuration options 2020-02-09 19:20:47 +09:00
gen-sql-config-docs.py [SPARK-30840][CORE][SQL] Add version property for ConfigEntry and ConfigBuilder 2020-02-22 09:46:42 +09:00
mkdocs.yml [SPARK-30731] Update deprecated Mkdocs option 2020-02-19 17:28:58 +09:00
README.md [SPARK-30510][SQL][DOCS] Publicly document Spark SQL configuration options 2020-02-09 19:20:47 +09:00

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.