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Maxim Gekk 8086acc2f6 [SPARK-24244][SQL] Passing only required columns to the CSV parser 
## What changes were proposed in this pull request?

uniVocity parser allows to specify only required column names or indexes for [parsing](https://www.univocity.com/pages/parsers-tutorial) like:

```
// Here we select only the columns by their indexes.
// The parser just skips the values in other columns
parserSettings.selectIndexes(4, 0, 1);
CsvParser parser = new CsvParser(parserSettings);
```
In this PR, I propose to extract indexes from required schema and pass them into the CSV parser. Benchmarks show the following improvements in parsing of 1000 columns:

```
Select 100 columns out of 1000: x1.76
Select 1 column out of 1000: x2
```

**Note**: Comparing to current implementation, the changes can return different result for malformed rows in the `DROPMALFORMED` and `FAILFAST` modes if only subset of all columns is requested. To have previous behavior, set `spark.sql.csv.parser.columnPruning.enabled` to `false`.

## How was this patch tested?

It was tested by new test which selects 3 columns out of 15, by existing tests and by new benchmarks.

Author: Maxim Gekk <maxim.gekk@databricks.com>

Closes #21296 from MaxGekk/csv-column-pruning.
2018-05-22 22:07:32 +08:00
..
catalyst [SPARK-24244][SQL] Passing only required columns to the CSV parser 2018-05-22 22:07:32 +08:00
core [SPARK-24244][SQL] Passing only required columns to the CSV parser 2018-05-22 22:07:32 +08:00
hive [SPARK-24312][SQL] Upgrade to 2.3.3 for Hive Metastore Client 2.3 2018-05-18 12:54:19 -07:00
hive-thriftserver [SPARK-20120][SQL][FOLLOW-UP] Better way to support spark-sql silent mode. 2018-05-22 08:20:59 -05:00
create-docs.sh [MINOR][DOCS] Minor doc fixes related with doc build and uses script dir in SQL doc gen script 2017-08-26 13:56:24 +09:00
gen-sql-markdown.py [SPARK-21485][FOLLOWUP][SQL][DOCS] Describes examples and arguments separately, and note/since in SQL built-in function documentation 2017-08-05 10:10:56 -07:00
mkdocs.yml [SPARK-21485][SQL][DOCS] Spark SQL documentation generation for built-in functions 2017-07-26 09:38:51 -07:00
README.md [MINOR][DOC] Fix some typos and grammar issues 2018-04-06 13:37:08 +08:00

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 an extension of SQLContext called HiveContext that allows 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.