1c7f0ab302
This PR aims to provide a way to skip/query corrupt JSON records. To do so, we introduce an internal column to hold corrupt records (the default name is `_corrupt_record`. This name can be changed by setting the value of `spark.sql.columnNameOfCorruptRecord`). When there is a parsing error, we will put the corrupt record in its unparsed format to the internal column. Users can skip/query this column through SQL.
* To query those corrupt records
```
-- For Hive parser
SELECT `_corrupt_record`
FROM jsonTable
WHERE `_corrupt_record` IS NOT NULL
-- For our SQL parser
SELECT _corrupt_record
FROM jsonTable
WHERE _corrupt_record IS NOT NULL
```
* To skip corrupt records and query regular records
```
-- For Hive parser
SELECT field1, field2
FROM jsonTable
WHERE `_corrupt_record` IS NULL
-- For our SQL parser
SELECT field1, field2
FROM jsonTable
WHERE _corrupt_record IS NULL
```
Generally, it is not recommended to change the name of the internal column. If the name has to be changed to avoid possible name conflicts, you can use `sqlContext.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, <new column name>)` or `sqlContext.sql(SET spark.sql.columnNameOfCorruptRecord=<new column name>)`.
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes #2680 from yhuai/corruptJsonRecord and squashes the following commits:
4c9828e [Yin Huai] Merge remote-tracking branch 'upstream/master' into corruptJsonRecord
309616a [Yin Huai] Change the default name of corrupt record to "_corrupt_record".
b4a3632 [Yin Huai] Merge remote-tracking branch 'upstream/master' into corruptJsonRecord
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|---|---|---|
| .. | ||
| catalyst | ||
| core | ||
| hive | ||
| hive-thriftserver | ||
| README.md | ||
Spark SQL
This module provides support for executing relational queries expressed in either SQL or a LINQ-like Scala DSL.
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 allows 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.
Other dependencies for developers
In order to create new hive test cases , you will need to set several environmental variables.
export HIVE_HOME="<path to>/hive/build/dist"
export HIVE_DEV_HOME="<path to>/hive/"
export HADOOP_HOME="<path to>/hadoop-1.0.4"
Using the console
An interactive scala console can be invoked by running sbt/sbt hive/console. From here you can execute queries and inspect the various stages of query optimization.
catalyst$ sbt/sbt hive/console
[info] Starting scala interpreter...
import org.apache.spark.sql.catalyst.analysis._
import org.apache.spark.sql.catalyst.dsl._
import org.apache.spark.sql.catalyst.errors._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.catalyst.rules._
import org.apache.spark.sql.catalyst.types._
import org.apache.spark.sql.catalyst.util._
import org.apache.spark.sql.execution
import org.apache.spark.sql.hive._
import org.apache.spark.sql.hive.TestHive._
Welcome to Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_45).
Type in expressions to have them evaluated.
Type :help for more information.
scala> val query = sql("SELECT * FROM (SELECT * FROM src) a")
query: org.apache.spark.sql.SchemaRDD =
== Query Plan ==
== Physical Plan ==
HiveTableScan [key#10,value#11], (MetastoreRelation default, src, None), None
Query results are RDDs and can be operated as such.
scala> query.collect()
res2: Array[org.apache.spark.sql.Row] = Array([238,val_238], [86,val_86], [311,val_311], [27,val_27]...
You can also build further queries on top of these RDDs using the query DSL.
scala> query.where('key === 100).collect()
res3: Array[org.apache.spark.sql.Row] = Array([100,val_100], [100,val_100])
From the console you can even write rules that transform query plans. For example, the above query has redundant project operators that aren't doing anything. This redundancy can be eliminated using the transform function that is available on all TreeNode objects.
scala> query.queryExecution.analyzed
res4: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [key#10,value#11]
Project [key#10,value#11]
MetastoreRelation default, src, None
scala> query.queryExecution.analyzed transform {
| case Project(projectList, child) if projectList == child.output => child
| }
res5: res17: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [key#10,value#11]
MetastoreRelation default, src, None