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Chao Sun 4cc7d9b8f1 [SPARK-36123][SQL] Parquet vectorized reader doesn't skip null values correctly 
### What changes were proposed in this pull request?

Fix the skipping values logic in Parquet vectorized reader when column index is effective, by considering nulls and only call `ParquetVectorUpdater.skipValues` when the values are non-null.

### Why are the changes needed?

Currently, the Parquet vectorized reader may not work correctly if column index filtering is effective, and the data page contains null values. For instance, let's say we have two columns `c1: BIGINT` and `c2: STRING`, and the following pages:
```
   * c1        500       500       500       500
   *  |---------|---------|---------|---------|
   *  |-------|-----|-----|---|---|---|---|---|
   * c2     400   300   300 200 200 200 200 200
```

and suppose we have a query like the following:
```sql
SELECT * FROM t WHERE c1 = 500
```

this will create a Parquet row range `[500, 1000)` which, when applied to `c2`, will require us to skip all the rows in `[400,500)`. However the current logic for skipping rows is via `updater.skipValues(n, valueReader)` which is incorrect since this skips the next `n` non-null values. In the case when nulls are present, this will not work correctly.

### Does this PR introduce _any_ user-facing change?

No

### How was this patch tested?

Added a new test in `ParquetColumnIndexSuite`.

Closes #33330 from sunchao/SPARK-36123-skip-nulls.

Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit e980c7a840)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
2021-07-14 18:14:33 +08:00
..
catalyst [SPARK-35780][SQL] Support DATE/TIMESTAMP literals across the full range 2021-07-14 18:11:53 +08:00
core [SPARK-36123][SQL] Parquet vectorized reader doesn't skip null values correctly 2021-07-14 18:14:33 +08:00
hive [SPARK-36012][SQL] Add null flag in SHOW CREATE TABLE 2021-07-09 01:21:57 +08:00
hive-thriftserver [SPARK-35780][SQL] Support DATE/TIMESTAMP literals across the full range 2021-07-14 18:11:53 +08:00
create-docs.sh [SPARK-34010][SQL][DODCS] Use python3 instead of python in SQL documentation build 2021-01-05 19:48:10 +09:00
gen-sql-api-docs.py [SPARK-34747][SQL][DOCS] Add virtual operators to the built-in function document 2021-03-19 10:19:26 +09:00
gen-sql-config-docs.py [SPARK-32194][PYTHON] Use proper exception classes instead of plain Exception 2021-05-26 11:54:40 +09:00
gen-sql-functions-docs.py
mkdocs.yml
README.md

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.