docs/sql-data-sources-parquet.md

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title: Parquet Files
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* Table of contents
{:toc}

[Parquet](http://parquet.io) is a columnar format that is supported by many other data processing systems.
Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema
of the original data. When reading Parquet files, all columns are automatically converted to be nullable for
compatibility reasons.

### Loading Data Programmatically

Using the data from the above example:

<div class="codetabs">

<div data-lang="scala"  markdown="1">
{% include_example basic_parquet_example scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}
</div>

<div data-lang="java"  markdown="1">
{% include_example basic_parquet_example java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}
</div>

<div data-lang="python"  markdown="1">

{% include_example basic_parquet_example python/sql/datasource.py %}
</div>

<div data-lang="r"  markdown="1">

{% include_example basic_parquet_example r/RSparkSQLExample.R %}

</div>

<div data-lang="SQL"  markdown="1">

{% highlight sql %}

CREATE TEMPORARY VIEW parquetTable
USING org.apache.spark.sql.parquet
OPTIONS (
  path "examples/src/main/resources/people.parquet"
)

SELECT * FROM parquetTable

{% endhighlight %}

</div>

</div>

### Partition Discovery

Table partitioning is a common optimization approach used in systems like Hive. In a partitioned
table, data are usually stored in different directories, with partitioning column values encoded in
the path of each partition directory. All built-in file sources (including Text/CSV/JSON/ORC/Parquet)
are able to discover and infer partitioning information automatically.
For example, we can store all our previously used
population data into a partitioned table using the following directory structure, with two extra
columns, `gender` and `country` as partitioning columns:

{% highlight text %}

path
└── to
    └── table
        ├── gender=male
        │   ├── ...
        │   │
        │   ├── country=US
        │   │   └── data.parquet
        │   ├── country=CN
        │   │   └── data.parquet
        │   └── ...
        └── gender=female
            ├── ...
            │
            ├── country=US
            │   └── data.parquet
            ├── country=CN
            │   └── data.parquet
            └── ...

{% endhighlight %}

By passing `path/to/table` to either `SparkSession.read.parquet` or `SparkSession.read.load`, Spark SQL
will automatically extract the partitioning information from the paths.
Now the schema of the returned DataFrame becomes:

{% highlight text %}

root
|-- name: string (nullable = true)
|-- age: long (nullable = true)
|-- gender: string (nullable = true)
|-- country: string (nullable = true)

{% endhighlight %}

Notice that the data types of the partitioning columns are automatically inferred. Currently,
numeric data types, date, timestamp and string type are supported. Sometimes users may not want
to automatically infer the data types of the partitioning columns. For these use cases, the
automatic type inference can be configured by
`spark.sql.sources.partitionColumnTypeInference.enabled`, which is default to `true`. When type
inference is disabled, string type will be used for the partitioning columns.

Starting from Spark 1.6.0, partition discovery only finds partitions under the given paths
by default. For the above example, if users pass `path/to/table/gender=male` to either
`SparkSession.read.parquet` or `SparkSession.read.load`, `gender` will not be considered as a
partitioning column. If users need to specify the base path that partition discovery
should start with, they can set `basePath` in the data source options. For example,
when `path/to/table/gender=male` is the path of the data and
users set `basePath` to `path/to/table/`, `gender` will be a partitioning column.

### Schema Merging

Like Protocol Buffer, Avro, and Thrift, Parquet also supports schema evolution. Users can start with
a simple schema, and gradually add more columns to the schema as needed. In this way, users may end
up with multiple Parquet files with different but mutually compatible schemas. The Parquet data
source is now able to automatically detect this case and merge schemas of all these files.

Since schema merging is a relatively expensive operation, and is not a necessity in most cases, we
turned it off by default starting from 1.5.0. You may enable it by

1. setting data source option `mergeSchema` to `true` when reading Parquet files (as shown in the
   examples below), or
2. setting the global SQL option `spark.sql.parquet.mergeSchema` to `true`.

<div class="codetabs">

<div data-lang="scala"  markdown="1">
{% include_example schema_merging scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}
</div>

<div data-lang="java"  markdown="1">
{% include_example schema_merging java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}
</div>

<div data-lang="python"  markdown="1">

{% include_example schema_merging python/sql/datasource.py %}
</div>

<div data-lang="r"  markdown="1">

{% include_example schema_merging r/RSparkSQLExample.R %}

</div>

</div>

### Hive metastore Parquet table conversion

When reading from Hive metastore Parquet tables and writing to non-partitioned Hive metastore
Parquet tables, Spark SQL will try to use its own Parquet support instead of Hive SerDe for
better performance. This behavior is controlled by the `spark.sql.hive.convertMetastoreParquet`
configuration, and is turned on by default.

#### Hive/Parquet Schema Reconciliation

There are two key differences between Hive and Parquet from the perspective of table schema
processing.

1. Hive is case insensitive, while Parquet is not
1. Hive considers all columns nullable, while nullability in Parquet is significant

Due to this reason, we must reconcile Hive metastore schema with Parquet schema when converting a
Hive metastore Parquet table to a Spark SQL Parquet table. The reconciliation rules are:

1. Fields that have the same name in both schema must have the same data type regardless of
   nullability. The reconciled field should have the data type of the Parquet side, so that
   nullability is respected.

1. The reconciled schema contains exactly those fields defined in Hive metastore schema.

   - Any fields that only appear in the Parquet schema are dropped in the reconciled schema.
   - Any fields that only appear in the Hive metastore schema are added as nullable field in the
     reconciled schema.

#### Metadata Refreshing

Spark SQL caches Parquet metadata for better performance. When Hive metastore Parquet table
conversion is enabled, metadata of those converted tables are also cached. If these tables are
updated by Hive or other external tools, you need to refresh them manually to ensure consistent
metadata.

<div class="codetabs">

<div data-lang="scala"  markdown="1">

{% highlight scala %}
// spark is an existing SparkSession
spark.catalog.refreshTable("my_table")
{% endhighlight %}

</div>

<div data-lang="java"  markdown="1">

{% highlight java %}
// spark is an existing SparkSession
spark.catalog().refreshTable("my_table");
{% endhighlight %}

</div>

<div data-lang="python"  markdown="1">

{% highlight python %}
# spark is an existing SparkSession
spark.catalog.refreshTable("my_table")
{% endhighlight %}

</div>

<div data-lang="r"  markdown="1">

{% highlight r %}
refreshTable("my_table")
{% endhighlight %}

</div>

<div data-lang="SQL"  markdown="1">

{% highlight sql %}
REFRESH TABLE my_table;
{% endhighlight %}

</div>

</div>

### Configuration

Configuration of Parquet can be done using the `setConf` method on `SparkSession` or by running
`SET key=value` commands using SQL.

<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th><th>Since Version</th></tr>
<tr>
  <td><code>spark.sql.parquet.binaryAsString</code></td>
  <td>false</td>
  <td>
    Some other Parquet-producing systems, in particular Impala, Hive, and older versions of Spark SQL, do
    not differentiate between binary data and strings when writing out the Parquet schema. This
    flag tells Spark SQL to interpret binary data as a string to provide compatibility with these systems.
  </td>
  <td>1.1.1</td>
</tr>
<tr>
  <td><code>spark.sql.parquet.int96AsTimestamp</code></td>
  <td>true</td>
  <td>
    Some Parquet-producing systems, in particular Impala and Hive, store Timestamp into INT96. This
    flag tells Spark SQL to interpret INT96 data as a timestamp to provide compatibility with these systems.
  </td>
  <td>1.3.0</td>
</tr>
<tr>
  <td><code>spark.sql.parquet.compression.codec</code></td>
  <td>snappy</td>
  <td>
    Sets the compression codec used when writing Parquet files. If either <code>compression</code> or
    <code>parquet.compression</code> is specified in the table-specific options/properties, the precedence would be
    <code>compression</code>, <code>parquet.compression</code>, <code>spark.sql.parquet.compression.codec</code>. Acceptable values include:
    none, uncompressed, snappy, gzip, lzo, brotli, lz4, zstd.
    Note that <code>zstd</code> requires <code>ZStandardCodec</code> to be installed before Hadoop 2.9.0, <code>brotli</code> requires
    <code>BrotliCodec</code> to be installed.
  </td>
  <td>1.1.1</td>
</tr>
<tr>
  <td><code>spark.sql.parquet.filterPushdown</code></td>
  <td>true</td>
  <td>Enables Parquet filter push-down optimization when set to true.</td>
  <td>1.2.0</td>
</tr>
<tr>
  <td><code>spark.sql.hive.convertMetastoreParquet</code></td>
  <td>true</td>
  <td>
    When set to false, Spark SQL will use the Hive SerDe for parquet tables instead of the built in
    support.
  </td>
  <td>1.1.1</td>
</tr>
<tr>
  <td><code>spark.sql.parquet.mergeSchema</code></td>
  <td>false</td>
  <td>
    <p>
      When true, the Parquet data source merges schemas collected from all data files, otherwise the
      schema is picked from the summary file or a random data file if no summary file is available.
    </p>
  </td>
  <td>1.5.0</td>
</tr>
<tr>
  <td><code>spark.sql.parquet.writeLegacyFormat</code></td>
  <td>false</td>
  <td>
    If true, data will be written in a way of Spark 1.4 and earlier. For example, decimal values
    will be written in Apache Parquet's fixed-length byte array format, which other systems such as
    Apache Hive and Apache Impala use. If false, the newer format in Parquet will be used. For
    example, decimals will be written in int-based format. If Parquet output is intended for use
    with systems that do not support this newer format, set to true.
  </td>
  <td>1.6.0</td>
</tr>
</table>