Spark SQL Guide

Generic File Source Options

These generic options/configurations are effective only when using file-based sources: parquet, orc, avro, json, csv, text.

Please note that the hierarchy of directories used in examples below are:

dir1/
 ├── dir2/
 │    └── file2.parquet (schema: <file: string>, content: "file2.parquet")
 └── file1.parquet (schema: <file, string>, content: "file1.parquet")
 └── file3.json (schema: <file, string>, content: "{'file':'corrupt.json'}")

Ignore Corrupt Files

Spark allows you to use spark.sql.files.ignoreCorruptFiles to ignore corrupt files while reading data from files. When set to true, the Spark jobs will continue to run when encountering corrupted files and the contents that have been read will still be returned.

To ignore corrupt files while reading data files, you can use:

// enable ignore corrupt files
spark.sql("set spark.sql.files.ignoreCorruptFiles=true")
// dir1/file3.json is corrupt from parquet's view
val testCorruptDF = spark.read.parquet(
  "examples/src/main/resources/dir1/",
  "examples/src/main/resources/dir1/dir2/")
testCorruptDF.show()
// +-------------+
// |         file|
// +-------------+
// |file1.parquet|
// |file2.parquet|
// +-------------+
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala" in the Spark repo.
// enable ignore corrupt files
spark.sql("set spark.sql.files.ignoreCorruptFiles=true");
// dir1/file3.json is corrupt from parquet's view
Dataset<Row> testCorruptDF = spark.read().parquet(
        "examples/src/main/resources/dir1/",
        "examples/src/main/resources/dir1/dir2/");
testCorruptDF.show();
// +-------------+
// |         file|
// +-------------+
// |file1.parquet|
// |file2.parquet|
// +-------------+
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java" in the Spark repo.
# enable ignore corrupt files
spark.sql("set spark.sql.files.ignoreCorruptFiles=true")
# dir1/file3.json is corrupt from parquet's view
test_corrupt_df = spark.read.parquet("examples/src/main/resources/dir1/",
                                     "examples/src/main/resources/dir1/dir2/")
test_corrupt_df.show()
# +-------------+
# |         file|
# +-------------+
# |file1.parquet|
# |file2.parquet|
# +-------------+
Find full example code at "examples/src/main/python/sql/datasource.py" in the Spark repo.
# enable ignore corrupt files
sql("set spark.sql.files.ignoreCorruptFiles=true")
# dir1/file3.json is corrupt from parquet's view
testCorruptDF <- read.parquet(c("examples/src/main/resources/dir1/", "examples/src/main/resources/dir1/dir2/"))
head(testCorruptDF)
#            file
# 1 file1.parquet
# 2 file2.parquet
Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.

Ignore Missing Files

Spark allows you to use spark.sql.files.ignoreMissingFiles to ignore missing files while reading data from files. Here, missing file really means the deleted file under directory after you construct the DataFrame. When set to true, the Spark jobs will continue to run when encountering missing files and the contents that have been read will still be returned.

Path Global Filter

pathGlobFilter is used to only include files with file names matching the pattern. The syntax follows org.apache.hadoop.fs.GlobFilter. It does not change the behavior of partition discovery.

To load files with paths matching a given glob pattern while keeping the behavior of partition discovery, you can use:

val testGlobFilterDF = spark.read.format("parquet")
  .option("pathGlobFilter", "*.parquet") // json file should be filtered out
  .load("examples/src/main/resources/dir1")
testGlobFilterDF.show()
// +-------------+
// |         file|
// +-------------+
// |file1.parquet|
// +-------------+
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala" in the Spark repo.
Dataset<Row> testGlobFilterDF = spark.read().format("parquet")
        .option("pathGlobFilter", "*.parquet") // json file should be filtered out
        .load("examples/src/main/resources/dir1");
testGlobFilterDF.show();
// +-------------+
// |         file|
// +-------------+
// |file1.parquet|
// +-------------+
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java" in the Spark repo.
df = spark.read.load("examples/src/main/resources/dir1",
                     format="parquet", pathGlobFilter="*.parquet")
df.show()
# +-------------+
# |         file|
# +-------------+
# |file1.parquet|
# +-------------+
Find full example code at "examples/src/main/python/sql/datasource.py" in the Spark repo.
df <- read.df("examples/src/main/resources/dir1", "parquet", pathGlobFilter = "*.parquet")
#            file
# 1 file1.parquet
Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.

Recursive File Lookup

recursiveFileLookup is used to recursively load files and it disables partition inferring. Its default value is false. If data source explicitly specifies the partitionSpec when recursiveFileLookup is true, exception will be thrown.

To load all files recursively, you can use:

val recursiveLoadedDF = spark.read.format("parquet")
  .option("recursiveFileLookup", "true")
  .load("examples/src/main/resources/dir1")
recursiveLoadedDF.show()
// +-------------+
// |         file|
// +-------------+
// |file1.parquet|
// |file2.parquet|
// +-------------+
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala" in the Spark repo.
Dataset<Row> recursiveLoadedDF = spark.read().format("parquet")
        .option("recursiveFileLookup", "true")
        .load("examples/src/main/resources/dir1");
recursiveLoadedDF.show();
// +-------------+
// |         file|
// +-------------+
// |file1.parquet|
// |file2.parquet|
// +-------------+
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java" in the Spark repo.
recursive_loaded_df = spark.read.format("parquet")\
    .option("recursiveFileLookup", "true")\
    .load("examples/src/main/resources/dir1")
recursive_loaded_df.show()
# +-------------+
# |         file|
# +-------------+
# |file1.parquet|
# |file2.parquet|
# +-------------+
Find full example code at "examples/src/main/python/sql/datasource.py" in the Spark repo.
recursiveLoadedDF <- read.df("examples/src/main/resources/dir1", "parquet", recursiveFileLookup = "true")
head(recursiveLoadedDF)
#            file
# 1 file1.parquet
# 2 file2.parquet
Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.

Modification Time Path Filters

modifiedBefore and modifiedAfter are options that can be applied together or separately in order to achieve greater granularity over which files may load during a Spark batch query. (Note that Structured Streaming file sources don’t support these options.)

When a timezone option is not provided, the timestamps will be interpreted according to the Spark session timezone (spark.sql.session.timeZone).

To load files with paths matching a given modified time range, you can use:

val beforeFilterDF = spark.read.format("parquet")
  // Files modified before 07/01/2020 at 05:30 are allowed
  .option("modifiedBefore", "2020-07-01T05:30:00")
  .load("examples/src/main/resources/dir1");
beforeFilterDF.show();
// +-------------+
// |         file|
// +-------------+
// |file1.parquet|
// +-------------+
val afterFilterDF = spark.read.format("parquet")
   // Files modified after 06/01/2020 at 05:30 are allowed
  .option("modifiedAfter", "2020-06-01T05:30:00")
  .load("examples/src/main/resources/dir1");
afterFilterDF.show();
// +-------------+
// |         file|
// +-------------+
// +-------------+
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala" in the Spark repo.
Dataset<Row> beforeFilterDF = spark.read().format("parquet")
        // Only load files modified before 7/1/2020 at 05:30
        .option("modifiedBefore", "2020-07-01T05:30:00")
        // Only load files modified after 6/1/2020 at 05:30
        .option("modifiedAfter", "2020-06-01T05:30:00")
        // Interpret both times above relative to CST timezone
        .option("timeZone", "CST")
        .load("examples/src/main/resources/dir1");
beforeFilterDF.show();
// +-------------+
// |         file|
// +-------------+
// |file1.parquet|
// +-------------+
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java" in the Spark repo.
# Only load files modified before 07/1/2050 @ 08:30:00
df = spark.read.load("examples/src/main/resources/dir1",
                     format="parquet", modifiedBefore="2050-07-01T08:30:00")
df.show()
# +-------------+
# |         file|
# +-------------+
# |file1.parquet|
# +-------------+
# Only load files modified after 06/01/2050 @ 08:30:00
df = spark.read.load("examples/src/main/resources/dir1",
                     format="parquet", modifiedAfter="2050-06-01T08:30:00")
df.show()
# +-------------+
# |         file|
# +-------------+
# +-------------+
Find full example code at "examples/src/main/python/sql/datasource.py" in the Spark repo.
beforeDF <- read.df("examples/src/main/resources/dir1", "parquet", modifiedBefore= "2020-07-01T05:30:00")
#            file
# 1 file1.parquet
afterDF <- read.df("examples/src/main/resources/dir1", "parquet", modifiedAfter = "2020-06-01T05:30:00")
#            file
Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.