e6d1406abd
In hive,the schema of partition may be difference from the table schema.When we use spark-sql to query the data of partition which schema is difference from the table schema,we will get the exceptions as the description of the [jira](https://issues.apache.org/jira/browse/SPARK-5498) .For example: * We take a look of the schema for the partition and the table ```sql DESCRIBE partition_test PARTITION (dt='1'); id int None name string None dt string None # Partition Information # col_name data_type comment dt string None ``` ``` DESCRIBE partition_test; OK id bigint None name string None dt string None # Partition Information # col_name data_type comment dt string None ``` * run the sql ```sql SELECT * FROM partition_test where dt='1'; ``` we will get the cast exception `java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.MutableLong cannot be cast to org.apache.spark.sql.catalyst.expressions.MutableInt` Author: jeanlyn <jeanlyn92@gmail.com> Closes #4289 from jeanlyn/schema and squashes the following commits: 9c8da74 [jeanlyn] fix style b41d6b9 [jeanlyn] fix compile errors 07d84b6 [jeanlyn] Merge branch 'master' into schema 535b0b6 [jeanlyn] reduce conflicts d6c93c5 [jeanlyn] fix bug 1e8b30c [jeanlyn] fix code style 0549759 [jeanlyn] fix code style c879aa1 [jeanlyn] clean the code 2a91a87 [jeanlyn] add more test case and clean the code 12d800d [jeanlyn] fix code style 63d170a [jeanlyn] fix compile problem 7470901 [jeanlyn] reduce conflicts afc7da5 [jeanlyn] make getConvertedOI compatible between 0.12.0 and 0.13.1 b1527d5 [jeanlyn] fix type mismatch 10744ca [jeanlyn] Insert a space after the start of the comment 3b27af3 [jeanlyn] SPARK-5498:fix bug when query the data when partition schema does not match table schema |
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Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, and Python, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page and project wiki. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.) More detailed documentation is available from the project site, at "Building Spark".
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1000:
scala> sc.parallelize(1 to 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1000:
>>> sc.parallelize(range(1000)).count()
Example Programs
Spark also comes with several sample programs in the examples directory.
To run one of them, use ./bin/run-example <class> [params]. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn-cluster" or "yarn-client" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the examples
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Running Tests
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
Please see the guidance on how to run all automated tests.
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions. See also "Third Party Hadoop Distributions" for guidance on building a Spark application that works with a particular distribution.
Configuration
Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.