b26e7b510b
### What changes were proposed in this pull request?
Infer the partitions schema by:
1. interring the common type over all partition part values, and
2. casting those values to the common type
Before the changes:
1. Spark creates a literal with most appropriate type for concrete partition value i.e. `part0=-0` -> `Literal(0, IntegerType)`, `part0=abc` -> `Literal(UTF8String.fromString("abc"), StringType)`.
2. Finds the common type for all literals of a partition column. For the example above, it is `StringType`.
3. Casts those literal to the desired type:
- `Cast(Literal(0, IntegerType), StringType)` -> `UTF8String.fromString("0")`
- `Cast(Literal(UTF8String.fromString("abc", StringType), StringType)` -> `UTF8String.fromString("abc")`
In the example, we get a partition part value "0" which is different from the original one "-0". Spark shouldn't modify partition part values of the string type because it can influence on query results.
Closes #31423
### Why are the changes needed?
The changes fix the bug demonstrated by the example:
1. There are partitioned parquet files (file format doesn't matter):
```
/private/var/folders/p3/dfs6mf655d7fnjrsjvldh0tc0000gn/T/spark-e09eae99-7ecf-4ab2-b99b-f63f8dea658d
├── _SUCCESS
├── part=-0
│ └── part-00001-02144398-2896-4d21-9628-a8743d098cb4.c000.snappy.parquet
└── part=AA
└── part-00000-02144398-2896-4d21-9628-a8743d098cb4.c000.snappy.parquet
```
placed to two partitions "AA" and **"-0"**.
2. When reading them w/o specified schema:
```
val df = spark.read.parquet(path)
df.printSchema()
root
|-- id: integer (nullable = true)
|-- part: string (nullable = true)
```
the inferred type of the partition column `part` is the **string** type.
3. The expected values in the column `part` are "AA" and "-0" but we get:
```
df.show(false)
+---+----+
|id |part|
+---+----+
|0 |AA |
|1 |0 |
+---+----+
```
So, Spark returns **"0"** instead of **"-0"**.
### Does this PR introduce _any_ user-facing change?
This PR can change query results.
### How was this patch tested?
By running new test and existing test suites:
```
$ build/sbt "test:testOnly *FileIndexSuite"
$ build/sbt "test:testOnly *ParquetV1PartitionDiscoverySuite"
$ build/sbt "test:testOnly *ParquetV2PartitionDiscoverySuite"
```
Closes #31549 from MaxGekk/fix-partition-file-index-2.
Authored-by: Max Gekk <max.gekk@gmail.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 | ||
| gen-sql-functions-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.