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author | gatorsmile <gatorsmile@gmail.com> | 2016-06-02 13:22:43 -0700 |
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committer | Shixiong Zhu <shixiong@databricks.com> | 2016-06-02 13:22:43 -0700 |
commit | 9aff6f3b1915523432b1921fdd30fa015ed5d670 (patch) | |
tree | 10295f61fdeee607d38c5ba71126c358c5da44f1 /sql/hive/src | |
parent | 8900c8d8ff1614b5ec5a2ce213832fa13462b4d4 (diff) | |
download | spark-9aff6f3b1915523432b1921fdd30fa015ed5d670.tar.gz spark-9aff6f3b1915523432b1921fdd30fa015ed5d670.tar.bz2 spark-9aff6f3b1915523432b1921fdd30fa015ed5d670.zip |
[SPARK-15515][SQL] Error Handling in Running SQL Directly On Files
#### What changes were proposed in this pull request?
This PR is to address the following issues:
- **ISSUE 1:** For ORC source format, we are reporting the strange error message when we did not enable Hive support:
```SQL
SQL Example:
select id from `org.apache.spark.sql.hive.orc`.`file_path`
Error Message:
Table or view not found: `org.apache.spark.sql.hive.orc`.`file_path`
```
Instead, we should issue the error message like:
```
Expected Error Message:
The ORC data source must be used with Hive support enabled
```
- **ISSUE 2:** For the Avro format, we report the strange error message like:
The example query is like
```SQL
SQL Example:
select id from `avro`.`file_path`
select id from `com.databricks.spark.avro`.`file_path`
Error Message:
Table or view not found: `com.databricks.spark.avro`.`file_path`
```
The desired message should be like:
```
Expected Error Message:
Failed to find data source: avro. Please use Spark package http://spark-packages.org/package/databricks/spark-avro"
```
- ~~**ISSUE 3:** Unable to detect incompatibility libraries for Spark 2.0 in Data Source Resolution. We report a strange error message:~~
**Update**: The latest code changes contains
- For JDBC format, we added an extra checking in the rule `ResolveRelations` of `Analyzer`. Without the PR, Spark will return the error message like: `Option 'url' not specified`. Now, we are reporting `Unsupported data source type for direct query on files: jdbc`
- Make data source format name case incensitive so that error handling behaves consistent with the normal cases.
- Added the test cases for all the supported formats.
#### How was this patch tested?
Added test cases to cover all the above issues
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes #13283 from gatorsmile/runSQLAgainstFile.
Diffstat (limited to 'sql/hive/src')
-rw-r--r-- | sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala | 48 |
1 files changed, 46 insertions, 2 deletions
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala index b5691450ca..24de223cf8 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala @@ -1247,11 +1247,12 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils with TestHiveSingleton { } } - test("run sql directly on files") { + test("run sql directly on files - parquet") { val df = spark.range(100).toDF() withTempPath(f => { df.write.parquet(f.getCanonicalPath) - checkAnswer(sql(s"select id from parquet.`${f.getCanonicalPath}`"), + // data source type is case insensitive + checkAnswer(sql(s"select id from Parquet.`${f.getCanonicalPath}`"), df) checkAnswer(sql(s"select id from `org.apache.spark.sql.parquet`.`${f.getCanonicalPath}`"), df) @@ -1260,6 +1261,49 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils with TestHiveSingleton { }) } + test("run sql directly on files - orc") { + val df = spark.range(100).toDF() + withTempPath(f => { + df.write.orc(f.getCanonicalPath) + // data source type is case insensitive + checkAnswer(sql(s"select id from ORC.`${f.getCanonicalPath}`"), + df) + checkAnswer(sql(s"select id from `org.apache.spark.sql.hive.orc`.`${f.getCanonicalPath}`"), + df) + checkAnswer(sql(s"select a.id from orc.`${f.getCanonicalPath}` as a"), + df) + }) + } + + test("run sql directly on files - csv") { + val df = spark.range(100).toDF() + withTempPath(f => { + df.write.csv(f.getCanonicalPath) + // data source type is case insensitive + checkAnswer(sql(s"select cast(_c0 as int) id from CSV.`${f.getCanonicalPath}`"), + df) + checkAnswer( + sql(s"select cast(_c0 as int) id from `com.databricks.spark.csv`.`${f.getCanonicalPath}`"), + df) + checkAnswer(sql(s"select cast(a._c0 as int) id from csv.`${f.getCanonicalPath}` as a"), + df) + }) + } + + test("run sql directly on files - json") { + val df = spark.range(100).toDF() + withTempPath(f => { + df.write.json(f.getCanonicalPath) + // data source type is case insensitive + checkAnswer(sql(s"select id from jsoN.`${f.getCanonicalPath}`"), + df) + checkAnswer(sql(s"select id from `org.apache.spark.sql.json`.`${f.getCanonicalPath}`"), + df) + checkAnswer(sql(s"select a.id from json.`${f.getCanonicalPath}` as a"), + df) + }) + } + test("SPARK-8976 Wrong Result for Rollup #1") { checkAnswer(sql( "SELECT count(*) AS cnt, key % 5, grouping_id() FROM src GROUP BY key%5 WITH ROLLUP"), |