ODIn/spark-instrumented-optimizer
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity
spark-instrumented-optimizer/sql
History
Pablo Langa bbb2cba615 [SPARK-32025][SQL] Csv schema inference problems with different types in the same column 
### What changes were proposed in this pull request?

This pull request fixes a bug present in the csv type inference.
We have problems when we have different types in the same column.

**Previously:**
```
$ cat /example/f1.csv
col1
43200000
true

spark.read.csv(path="file:///example/*.csv", header=True, inferSchema=True).show()
+----+
|col1|
+----+
|null|
|true|
+----+

root
 |-- col1: boolean (nullable = true)
```
**Now**
```
spark.read.csv(path="file:///example/*.csv", header=True, inferSchema=True).show()
+-------------+
|col1          |
+-------------+
|43200000 |
|true           |
+-------------+

root
 |-- col1: string (nullable = true)
```

Previously the hierarchy of type inference is the following:

> IntegerType
> > LongType
> > > DecimalType
> > > > DoubleType
> > > > > TimestampType
> > > > > > BooleanType
> > > > > > > StringType

So, when, for example, we have integers in one column, and the last element is a boolean, all the column is inferred as a boolean column incorrectly and all the number are shown as null when you see the data

We need the following hierarchy. When we have different numeric types in the column it will be resolved correctly. And when we have other different types it will be resolved as a String type column
> IntegerType
> > LongType
> > > DecimalType
> > > > DoubleType
> > > > > StringType

> TimestampType
> > StringType

> BooleanType
> > StringType

> StringType

### Why are the changes needed?

Fix the bug explained

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

No

### How was this patch tested?

Unit test and manual tests

Closes #28896 from planga82/feature/SPARK-32025_csv_inference.

Authored-by: Pablo Langa <soypab@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-06-26 10:41:27 +09:00
..
catalyst [SPARK-32025][SQL] Csv schema inference problems with different types in the same column 2020-06-26 10:41:27 +09:00
core [SPARK-32025][SQL] Csv schema inference problems with different types in the same column 2020-06-26 10:41:27 +09:00
hive [SPARK-31710][SQL] Fail casting numeric to timestamp by default 2020-06-16 08:35:35 +00:00
hive-thriftserver [SPARK-32034][SQL] Port HIVE-14817: Shutdown the SessionManager timeoutChecker thread properly upon shutdown 2020-06-21 16:28:00 -07:00
create-docs.sh [SPARK-31550][SQL][DOCS] Set nondeterministic configurations with general meanings in sql configuration doc 2020-04-27 17:08:52 +09:00
gen-sql-api-docs.py [SPARK-31474][SQL][FOLLOWUP] Replace _FUNC_ placeholder with functionname in the note field of expression info 2020-04-23 13:33:04 +09:00
gen-sql-config-docs.py [SPARK-31550][SQL][DOCS] Set nondeterministic configurations with general meanings in sql configuration doc 2020-04-27 17:08:52 +09:00
gen-sql-functions-docs.py [SPARK-31562][SQL] Update ExpressionDescription for substring, current_date, and current_timestamp 2020-04-26 11:46:52 -07:00
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.