Parquet format - Azure Data Factory & Azure Synapse

Using Self-hosted Integration Runtime

Important

For copy empowered by Self-hosted Integration Runtime e.g. between on-premises and cloud data stores, if you are not copying Parquet files as-is , you need to install the 64-bit JRE 8 (Java Runtime Environment) or OpenJDK on your IR machine. Check the following paragraph with more details.

For copy running on Self-hosted IR with Parquet file serialization/deserialization, the service locates the Java runtime by firstly checking the registry (SOFTWARE\JavaSoft\Java Runtime Environment\{Current Version}\JavaHome) for JRE, if not found, secondly checking system variable JAVA_HOME for OpenJDK.

To use JRE : The 64-bit IR requires 64-bit JRE. You can find it from here .

To use OpenJDK : It's supported since IR version 3.13. Package the jvm.dll with all other required assemblies of OpenJDK into Self-hosted IR machine, and set system environment variable JAVA_HOME accordingly, and then restart Self-hosted IR for taking effect immediately.

If you copy data to/from Parquet format using Self-hosted Integration Runtime and hit error saying "An error occurred when invoking java, message: java.lang.OutOfMemoryError:Java heap space ", you can add an environment variable _JAVA_OPTIONS in the machine that hosts the Self-hosted IR to adjust the min/max heap size for JVM to empower such copy, then rerun the pipeline.

Example: set variable _JAVA_OPTIONS with value -Xms256m -Xmx16g . The flag Xms specifies the initial memory allocation pool for a Java Virtual Machine (JVM), while Xmx specifies the maximum memory allocation pool. This means that JVM will be started with Xms amount of memory and will be able to use a maximum of Xmx amount of memory. By default, the service uses min 64 MB and max 1G.

Dataset properties

For a full list of sections and properties available for defining datasets, see the Datasets article. This section provides a list of properties supported by the Parquet dataset.

Property Description Required location Location settings of the file(s). Each file-based connector has its own location type and supported properties under


   location

. See details in connector article -> Dataset properties section . compressionCodec The compression codec to use when writing to Parquet files. When reading from Parquet files, Data Factories automatically determine the compression codec based on the file metadata.
Supported types are " none ", " gzip ", " snappy " (default), and " lzo ". Note currently Copy activity doesn't support LZO when read/write Parquet files.

White space in column name is not supported for Parquet files.

Below is an example of Parquet dataset on Azure Blob Storage:

"name": "ParquetDataset", "properties": { "type": "Parquet", "linkedServiceName": { "referenceName": "<Azure Blob Storage linked service name>", "type": "LinkedServiceReference" "schema": [ < physical schema, optional, retrievable during authoring > ], "typeProperties": { "location": { "type": "AzureBlobStorageLocation", "container": "containername", "folderPath": "folder/subfolder", "compressionCodec": "snappy"

Copy activity properties

For a full list of sections and properties available for defining activities, see the Pipelines article. This section provides a list of properties supported by the Parquet source and sink.

Parquet as source

The following properties are supported in the copy activity *source* section.

Property Description Required storeSettings A group of properties on how to read data from a data store. Each file-based connector has its own supported read settings under


   storeSettings

. See details in connector article -> Copy activity properties section .

Parquet as sink

The following properties are supported in the copy activity *sink* section.

Property Description Required storeSettings A group of properties on how to write data to a data store. Each file-based connector has its own supported write settings under


   storeSettings

. See details in connector article -> Copy activity properties section .

Supported Parquet write settings under formatSettings :

Property Description Required maxRowsPerFile When writing data into a folder, you can choose to write to multiple files and specify the max rows per file. fileNamePrefix Applicable when


   maxRowsPerFile

is configured.
Specify the file name prefix when writing data to multiple files, resulted in this pattern:


   <fileNamePrefix>_00000.<fileExtension>

. If not specified, file name prefix will be auto generated. This property does not apply when source is file-based store or partition-option-enabled data store .

Mapping data flow properties

In mapping data flows, you can read and write to parquet format in the following data stores: Azure Blob Storage , Azure Data Lake Storage Gen1 , Azure Data Lake Storage Gen2 and SFTP , and you can read parquet format in Amazon S3 .

Source properties

The below table lists the properties supported by a parquet source. You can edit these properties in the Source options tab.

Description Required Allowed values Data flow script property Wild card paths All files matching the wildcard path will be processed. Overrides the folder and file path set in the dataset. String[] wildcardPaths Partition root path For file data that is partitioned, you can enter a partition root path in order to read partitioned folders as columns String partitionRootPath List of files Whether your source is pointing to a text file that lists files to process


   true


   false

fileList Column to store file name Create a new column with the source file name and path String rowUrlColumn After completion Delete or move the files after processing. File path starts from the container root Delete:


   true


   false

Move:


   [<from>, <to>]

purgeFiles
moveFiles Filter by last modified Choose to filter files based upon when they were last altered Timestamp modifiedAfter
modifiedBefore Allow no files found If true, an error is not thrown if no files are found


   true


   false

ignoreNoFilesFound

Source example

The below image is an example of a parquet source configuration in mapping data flows.

File name option The naming format of the data written. By default, one file per partition in format


   part-#####-tid-<guid>

Pattern: String
Per partition: String[]
As data in column: String
Output to single file:


   ['<fileName>']

filePattern
partitionFileNames
rowUrlColumn
partitionFileNames

Sink example

The below image is an example of a parquet sink configuration in mapping data flows.

validateSchema: false, skipDuplicateMapInputs: true, skipDuplicateMapOutputs: true) ~> ParquetSink

Data type support

Parquet complex data types (e.g. MAP, LIST, STRUCT) are currently supported only in Data Flows, not in Copy Activity. To use complex types in data flows, do not import the file schema in the dataset, leaving schema blank in the dataset. Then, in the Source transformation, import the projection.

Next steps

Copy activity overview

Mapping data flow

Lookup activity

GetMetadata activity