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author | Reynold Xin <rxin@databricks.com> | 2016-07-01 15:16:04 -0700 |
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committer | Reynold Xin <rxin@databricks.com> | 2016-07-01 15:16:04 -0700 |
commit | d601894c0494d415e7f330e02168c43a2dacfb02 (patch) | |
tree | 8c365e808a5f4c6836a20a5c510332e380a1ee2f | |
parent | e4fa58c43ce2bf8d76bffb0d9dc1132f8d0eae6a (diff) | |
download | spark-d601894c0494d415e7f330e02168c43a2dacfb02.tar.gz spark-d601894c0494d415e7f330e02168c43a2dacfb02.tar.bz2 spark-d601894c0494d415e7f330e02168c43a2dacfb02.zip |
[SPARK-16335][SQL] Structured streaming should fail if source directory does not exist
## What changes were proposed in this pull request?
In structured streaming, Spark does not report errors when the specified directory does not exist. This is a behavior different from the batch mode. This patch changes the behavior to fail if the directory does not exist (when the path is not a glob pattern).
## How was this patch tested?
Updated unit tests to reflect the new behavior.
Author: Reynold Xin <rxin@databricks.com>
Closes #14002 from rxin/SPARK-16335.
4 files changed, 36 insertions, 35 deletions
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala index bb1793d451..90c71cc6cf 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala @@ -232,6 +232,10 @@ class SparkHadoopUtil extends Logging { recurse(baseStatus) } + def isGlobPath(pattern: Path): Boolean = { + pattern.toString.exists("{}[]*?\\".toSet.contains) + } + def globPath(pattern: Path): Seq[Path] = { val fs = pattern.getFileSystem(conf) Option(fs.globStatus(pattern)).map { statuses => @@ -240,11 +244,7 @@ class SparkHadoopUtil extends Logging { } def globPathIfNecessary(pattern: Path): Seq[Path] = { - if (pattern.toString.exists("{}[]*?\\".toSet.contains)) { - globPath(pattern) - } else { - Seq(pattern) - } + if (isGlobPath(pattern)) globPath(pattern) else Seq(pattern) } /** diff --git a/python/pyspark/sql/streaming.py b/python/pyspark/sql/streaming.py index bffe398247..8bac347e13 100644 --- a/python/pyspark/sql/streaming.py +++ b/python/pyspark/sql/streaming.py @@ -315,7 +315,7 @@ class DataStreamReader(OptionUtils): >>> json_sdf = spark.readStream.format("json")\ .schema(sdf_schema)\ - .load(os.path.join(tempfile.mkdtemp(),'data')) + .load(tempfile.mkdtemp()) >>> json_sdf.isStreaming True >>> json_sdf.schema == sdf_schema @@ -382,8 +382,7 @@ class DataStreamReader(OptionUtils): it uses the value specified in ``spark.sql.columnNameOfCorruptRecord``. - >>> json_sdf = spark.readStream.json(os.path.join(tempfile.mkdtemp(), 'data'), \ - schema = sdf_schema) + >>> json_sdf = spark.readStream.json(tempfile.mkdtemp(), schema = sdf_schema) >>> json_sdf.isStreaming True >>> json_sdf.schema == sdf_schema @@ -411,8 +410,7 @@ class DataStreamReader(OptionUtils): .. note:: Experimental. - >>> parquet_sdf = spark.readStream.schema(sdf_schema)\ - .parquet(os.path.join(tempfile.mkdtemp())) + >>> parquet_sdf = spark.readStream.schema(sdf_schema).parquet(tempfile.mkdtemp()) >>> parquet_sdf.isStreaming True >>> parquet_sdf.schema == sdf_schema @@ -512,8 +510,7 @@ class DataStreamReader(OptionUtils): * ``DROPMALFORMED`` : ignores the whole corrupted records. * ``FAILFAST`` : throws an exception when it meets corrupted records. - >>> csv_sdf = spark.readStream.csv(os.path.join(tempfile.mkdtemp(), 'data'), \ - schema = sdf_schema) + >>> csv_sdf = spark.readStream.csv(tempfile.mkdtemp(), schema = sdf_schema) >>> csv_sdf.isStreaming True >>> csv_sdf.schema == sdf_schema diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala index a4110d7b11..6dc27c1952 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala @@ -203,6 +203,18 @@ case class DataSource( val path = caseInsensitiveOptions.getOrElse("path", { throw new IllegalArgumentException("'path' is not specified") }) + + // Check whether the path exists if it is not a glob pattern. + // For glob pattern, we do not check it because the glob pattern might only make sense + // once the streaming job starts and some upstream source starts dropping data. + val hdfsPath = new Path(path) + if (!SparkHadoopUtil.get.isGlobPath(hdfsPath)) { + val fs = hdfsPath.getFileSystem(sparkSession.sessionState.newHadoopConf()) + if (!fs.exists(hdfsPath)) { + throw new AnalysisException(s"Path does not exist: $path") + } + } + val isSchemaInferenceEnabled = sparkSession.conf.get(SQLConf.STREAMING_SCHEMA_INFERENCE) val isTextSource = providingClass == classOf[text.TextFileFormat] // If the schema inference is disabled, only text sources require schema to be specified diff --git a/sql/core/src/test/scala/org/apache/spark/sql/streaming/FileStreamSourceSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/streaming/FileStreamSourceSuite.scala index 6c04846f00..8a34cf95f9 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/streaming/FileStreamSourceSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/streaming/FileStreamSourceSuite.scala @@ -179,18 +179,24 @@ class FileStreamSourceSuite extends FileStreamSourceTest { withSQLConf(SQLConf.STREAMING_SCHEMA_INFERENCE.key -> "true") { testError() } } - test("FileStreamSource schema: path doesn't exist, no schema") { - val e = intercept[IllegalArgumentException] { - createFileStreamSourceAndGetSchema(format = None, path = Some("/a/b/c"), schema = None) + test("FileStreamSource schema: path doesn't exist (without schema) should throw exception") { + withTempDir { dir => + intercept[AnalysisException] { + val userSchema = new StructType().add(new StructField("value", IntegerType)) + val schema = createFileStreamSourceAndGetSchema( + format = None, path = Some(new File(dir, "1").getAbsolutePath), schema = None) + } } - assert(e.getMessage.toLowerCase.contains("schema")) // reason is schema absence, not the path } - test("FileStreamSource schema: path doesn't exist, with schema") { - val userSchema = new StructType().add(new StructField("value", IntegerType)) - val schema = createFileStreamSourceAndGetSchema( - format = None, path = Some("/a/b/c"), schema = Some(userSchema)) - assert(schema === userSchema) + test("FileStreamSource schema: path doesn't exist (with schema) should throw exception") { + withTempDir { dir => + intercept[AnalysisException] { + val userSchema = new StructType().add(new StructField("value", IntegerType)) + val schema = createFileStreamSourceAndGetSchema( + format = None, path = Some(new File(dir, "1").getAbsolutePath), schema = Some(userSchema)) + } + } } @@ -225,20 +231,6 @@ class FileStreamSourceSuite extends FileStreamSourceTest { // =============== Parquet file stream schema tests ================ - ignore("FileStreamSource schema: parquet, no existing files, no schema") { - withTempDir { src => - withSQLConf(SQLConf.STREAMING_SCHEMA_INFERENCE.key -> "true") { - val e = intercept[AnalysisException] { - createFileStreamSourceAndGetSchema( - format = Some("parquet"), - path = Some(new File(src, "1").getCanonicalPath), - schema = None) - } - assert("Unable to infer schema. It must be specified manually.;" === e.getMessage) - } - } - } - test("FileStreamSource schema: parquet, existing files, no schema") { withTempDir { src => Seq("a", "b", "c").toDS().as("userColumn").toDF().write |