spark-instrumented-optimizer/sql
Maxim Gekk 776f299fc8 [SPARK-24709][SQL] schema_of_json() - schema inference from an example
## What changes were proposed in this pull request?

In the PR, I propose to add new function - *schema_of_json()* which infers schema of JSON string literal. The result of the function is a string containing a schema in DDL format.

One of the use cases is using of *schema_of_json()* in the combination with *from_json()*. Currently, _from_json()_ requires a schema as a mandatory argument. The *schema_of_json()* function will allow to point out an JSON string as an example which has the same schema as the first argument of _from_json()_. For instance:

```sql
select from_json(json_column, schema_of_json('{"c1": [0], "c2": [{"c3":0}]}'))
from json_table;
```

## How was this patch tested?

Added new test to `JsonFunctionsSuite`, `JsonExpressionsSuite` and SQL tests to `json-functions.sql`

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

Closes #21686 from MaxGekk/infer_schema_json.
2018-07-04 09:38:18 +08:00
..
catalyst [SPARK-24709][SQL] schema_of_json() - schema inference from an example 2018-07-04 09:38:18 +08:00
core [SPARK-24709][SQL] schema_of_json() - schema inference from an example 2018-07-04 09:38:18 +08:00
hive [SPARK-24204][SQL] Verify a schema in Json/Orc/ParquetFileFormat 2018-06-27 15:25:51 -07:00
hive-thriftserver [SPARK-24553][WEB-UI] http 302 fixes for href redirect 2018-06-27 15:36:59 -07:00
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

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.