Generic Load/Save Functions
- Manually Specifying Options
- Run SQL on files directly
- Save Modes
- Saving to Persistent Tables
- Bucketing, Sorting and Partitioning
In the simplest form, the default data source (parquet unless otherwise configured by
spark.sql.sources.default) will be used for all operations.
val usersDF = spark.read.load("examples/src/main/resources/users.parquet")
usersDF.select("name", "favorite_color").write.save("namesAndFavColors.parquet")Dataset<Row> usersDF = spark.read().load("examples/src/main/resources/users.parquet");
usersDF.select("name", "favorite_color").write().save("namesAndFavColors.parquet");df = spark.read.load("examples/src/main/resources/users.parquet")
df.select("name", "favorite_color").write.save("namesAndFavColors.parquet")df <- read.df("examples/src/main/resources/users.parquet")
write.df(select(df, "name", "favorite_color"), "namesAndFavColors.parquet")Manually Specifying Options
You can also manually specify the data source that will be used along with any extra options
that you would like to pass to the data source. Data sources are specified by their fully qualified
name (i.e., org.apache.spark.sql.parquet), but for built-in sources you can also use their short
names (json, parquet, jdbc, orc, libsvm, csv, text). DataFrames loaded from any data
source type can be converted into other types using this syntax.
Please refer the API documentation for available options of built-in sources, for example,
org.apache.spark.sql.DataFrameReader and org.apache.spark.sql.DataFrameWriter. The
options documented there should be applicable through non-Scala Spark APIs (e.g. PySpark)
as well. For other formats, refer to the API documentation of the particular format.
To load a JSON file you can use:
val peopleDF = spark.read.format("json").load("examples/src/main/resources/people.json")
peopleDF.select("name", "age").write.format("parquet").save("namesAndAges.parquet")Dataset<Row> peopleDF =
spark.read().format("json").load("examples/src/main/resources/people.json");
peopleDF.select("name", "age").write().format("parquet").save("namesAndAges.parquet");df = spark.read.load("examples/src/main/resources/people.json", format="json")
df.select("name", "age").write.save("namesAndAges.parquet", format="parquet")df <- read.df("examples/src/main/resources/people.json", "json")
namesAndAges <- select(df, "name", "age")
write.df(namesAndAges, "namesAndAges.parquet", "parquet")To load a CSV file you can use:
val peopleDFCsv = spark.read.format("csv")
.option("sep", ";")
.option("inferSchema", "true")
.option("header", "true")
.load("examples/src/main/resources/people.csv")Dataset<Row> peopleDFCsv = spark.read().format("csv")
.option("sep", ";")
.option("inferSchema", "true")
.option("header", "true")
.load("examples/src/main/resources/people.csv");df = spark.read.load("examples/src/main/resources/people.csv",
format="csv", sep=";", inferSchema="true", header="true")df <- read.df("examples/src/main/resources/people.csv", "csv", sep = ";", inferSchema = TRUE, header = TRUE)
namesAndAges <- select(df, "name", "age")The extra options are also used during write operation.
For example, you can control bloom filters and dictionary encodings for ORC data sources.
The following ORC example will create bloom filter and use dictionary encoding only for favorite_color.
For Parquet, there exists parquet.bloom.filter.enabled and parquet.enable.dictionary, too.
To find more detailed information about the extra ORC/Parquet options,
visit the official Apache ORC / Parquet websites.
ORC data source:
usersDF.write.format("orc")
.option("orc.bloom.filter.columns", "favorite_color")
.option("orc.dictionary.key.threshold", "1.0")
.option("orc.column.encoding.direct", "name")
.save("users_with_options.orc")usersDF.write().format("orc")
.option("orc.bloom.filter.columns", "favorite_color")
.option("orc.dictionary.key.threshold", "1.0")
.option("orc.column.encoding.direct", "name")
.save("users_with_options.orc");df = spark.read.orc("examples/src/main/resources/users.orc")
(df.write.format("orc")
.option("orc.bloom.filter.columns", "favorite_color")
.option("orc.dictionary.key.threshold", "1.0")
.option("orc.column.encoding.direct", "name")
.save("users_with_options.orc"))df <- read.df("examples/src/main/resources/users.orc", "orc")
write.orc(df, "users_with_options.orc", orc.bloom.filter.columns = "favorite_color", orc.dictionary.key.threshold = 1.0, orc.column.encoding.direct = "name")CREATE TABLE users_with_options (
name STRING,
favorite_color STRING,
favorite_numbers array<integer>
) USING ORC
OPTIONS (
orc.bloom.filter.columns 'favorite_color',
orc.dictionary.key.threshold '1.0',
orc.column.encoding.direct 'name'
)Parquet data source:
usersDF.write.format("parquet")
.option("parquet.bloom.filter.enabled#favorite_color", "true")
.option("parquet.bloom.filter.expected.ndv#favorite_color", "1000000")
.option("parquet.enable.dictionary", "true")
.option("parquet.page.write-checksum.enabled", "false")
.save("users_with_options.parquet")usersDF.write().format("parquet")
.option("parquet.bloom.filter.enabled#favorite_color", "true")
.option("parquet.bloom.filter.expected.ndv#favorite_color", "1000000")
.option("parquet.enable.dictionary", "true")
.option("parquet.page.write-checksum.enabled", "false")
.save("users_with_options.parquet");df = spark.read.parquet("examples/src/main/resources/users.parquet")
(df.write.format("parquet")
.option("parquet.bloom.filter.enabled#favorite_color", "true")
.option("parquet.bloom.filter.expected.ndv#favorite_color", "1000000")
.option("parquet.enable.dictionary", "true")
.option("parquet.page.write-checksum.enabled", "false")
.save("users_with_options.parquet"))df <- read.df("examples/src/main/resources/users.parquet", "parquet")
write.parquet(df, "users_with_options.parquet", parquet.bloom.filter.enabled#favorite_color = true, parquet.bloom.filter.expected.ndv#favorite_color = 1000000, parquet.enable.dictionary = true, parquet.page.write-checksum.enabled = false)CREATE TABLE users_with_options (
name STRING,
favorite_color STRING,
favorite_numbers array<integer>
) USING parquet
OPTIONS (
`parquet.bloom.filter.enabled#favorite_color` true,
`parquet.bloom.filter.expected.ndv#favorite_color` 1000000,
parquet.enable.dictionary true,
parquet.page.write-checksum.enabled true
)Run SQL on files directly
Instead of using read API to load a file into DataFrame and query it, you can also query that file directly with SQL.
val sqlDF = spark.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`")Dataset<Row> sqlDF =
spark.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`");df = spark.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`")df <- sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`")Save Modes
Save operations can optionally take a SaveMode, that specifies how to handle existing data if
present. It is important to realize that these save modes do not utilize any locking and are not
atomic. Additionally, when performing an Overwrite, the data will be deleted before writing out the
new data.
| Scala/Java | Any Language | Meaning |
|---|---|---|
SaveMode.ErrorIfExists (default) |
"error" or "errorifexists" (default) |
When saving a DataFrame to a data source, if data already exists, an exception is expected to be thrown. |
SaveMode.Append |
"append" |
When saving a DataFrame to a data source, if data/table already exists, contents of the DataFrame are expected to be appended to existing data. |
SaveMode.Overwrite |
"overwrite" |
Overwrite mode means that when saving a DataFrame to a data source, if data/table already exists, existing data is expected to be overwritten by the contents of the DataFrame. |
SaveMode.Ignore |
"ignore" |
Ignore mode means that when saving a DataFrame to a data source, if data already exists,
the save operation is expected not to save the contents of the DataFrame and not to
change the existing data. This is similar to a CREATE TABLE IF NOT EXISTS in SQL.
|
Saving to Persistent Tables
DataFrames can also be saved as persistent tables into Hive metastore using the saveAsTable
command. Notice that an existing Hive deployment is not necessary to use this feature. Spark will create a
default local Hive metastore (using Derby) for you. Unlike the createOrReplaceTempView command,
saveAsTable will materialize the contents of the DataFrame and create a pointer to the data in the
Hive metastore. Persistent tables will still exist even after your Spark program has restarted, as
long as you maintain your connection to the same metastore. A DataFrame for a persistent table can
be created by calling the table method on a SparkSession with the name of the table.
For file-based data source, e.g. text, parquet, json, etc. you can specify a custom table path via the
path option, e.g. df.write.option("path", "/some/path").saveAsTable("t"). When the table is dropped,
the custom table path will not be removed and the table data is still there. If no custom table path is
specified, Spark will write data to a default table path under the warehouse directory. When the table is
dropped, the default table path will be removed too.
Starting from Spark 2.1, persistent datasource tables have per-partition metadata stored in the Hive metastore. This brings several benefits:
- Since the metastore can return only necessary partitions for a query, discovering all the partitions on the first query to the table is no longer needed.
- Hive DDLs such as
ALTER TABLE PARTITION ... SET LOCATIONare now available for tables created with the Datasource API.
Note that partition information is not gathered by default when creating external datasource tables (those with a path option). To sync the partition information in the metastore, you can invoke MSCK REPAIR TABLE.
Bucketing, Sorting and Partitioning
For file-based data source, it is also possible to bucket and sort or partition the output. Bucketing and sorting are applicable only to persistent tables:
peopleDF.write.bucketBy(42, "name").sortBy("age").saveAsTable("people_bucketed")peopleDF.write().bucketBy(42, "name").sortBy("age").saveAsTable("people_bucketed");df.write.bucketBy(42, "name").sortBy("age").saveAsTable("people_bucketed")CREATE TABLE users_bucketed_by_name(
name STRING,
favorite_color STRING,
favorite_numbers array<integer>
) USING parquet
CLUSTERED BY(name) INTO 42 BUCKETS;while partitioning can be used with both save and saveAsTable when using the Dataset APIs.
usersDF.write.partitionBy("favorite_color").format("parquet").save("namesPartByColor.parquet")usersDF
.write()
.partitionBy("favorite_color")
.format("parquet")
.save("namesPartByColor.parquet");df.write.partitionBy("favorite_color").format("parquet").save("namesPartByColor.parquet")CREATE TABLE users_by_favorite_color(
name STRING,
favorite_color STRING,
favorite_numbers array<integer>
) USING csv PARTITIONED BY(favorite_color);It is possible to use both partitioning and bucketing for a single table:
usersDF
.write
.partitionBy("favorite_color")
.bucketBy(42, "name")
.saveAsTable("users_partitioned_bucketed")usersDF
.write()
.partitionBy("favorite_color")
.bucketBy(42, "name")
.saveAsTable("users_partitioned_bucketed");df = spark.read.parquet("examples/src/main/resources/users.parquet")
(df
.write
.partitionBy("favorite_color")
.bucketBy(42, "name")
.saveAsTable("users_partitioned_bucketed"))CREATE TABLE users_bucketed_and_partitioned(
name STRING,
favorite_color STRING,
favorite_numbers array<integer>
) USING parquet
PARTITIONED BY (favorite_color)
CLUSTERED BY(name) SORTED BY (favorite_numbers) INTO 42 BUCKETS;partitionBy creates a directory structure as described in the Partition Discovery section.
Thus, it has limited applicability to columns with high cardinality. In contrast
bucketBy distributes
data across a fixed number of buckets and can be used when the number of unique values is unbounded.