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Move data from PostgreSQL using Azure Data Factory

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Note

This article applies to version 1 of Data Factory. If you are using the current version of the Data Factory service, see PostgreSQL connector in V2.

This article explains how to use the Copy Activity in Azure Data Factory to move data from an on-premises PostgreSQL database. It builds on the Data Movement Activities article, which presents a general overview of data movement with the copy activity.

You can copy data from an on-premises PostgreSQL data store to any supported sink data store. For a list of data stores supported as sinks by the copy activity, see supported data stores. Data factory currently supports moving data from a PostgreSQL database to other data stores, but not for moving data from other data stores to an PostgreSQL database.

Prerequisites

Data Factory service supports connecting to on-premises PostgreSQL sources using the Data Management Gateway. See moving data between on-premises locations and cloud article to learn about Data Management Gateway and step-by-step instructions on setting up the gateway.

Gateway is required even if the PostgreSQL database is hosted in an Azure IaaS VM. You can install gateway on the same IaaS VM as the data store or on a different VM as long as the gateway can connect to the database.

Note

See Troubleshoot gateway issues for tips on troubleshooting connection/gateway related issues.

Supported versions and installation

For Data Management Gateway to connect to the PostgreSQL Database, install the Ngpsql data provider for PostgreSQL with version between 2.0.12 and 3.1.9 on the same system as the Data Management Gateway. PostgreSQL version 7.4 and above is supported.

Getting started

You can create a pipeline with a copy activity that moves data from an on-premises PostgreSQL data store by using different tools/APIs.

  • The easiest way to create a pipeline is to use the Copy Wizard. See Tutorial: Create a pipeline using Copy Wizard for a quick walkthrough on creating a pipeline using the Copy data wizard.
  • You can also use the following tools to create a pipeline:

    • Visual Studio

    • Azure PowerShell

    • Azure Resource Manager template

    • .NET API

    • REST API

      See Copy activity tutorial for step-by-step instructions to create a pipeline with a copy activity.

Whether you use the tools or APIs, you perform the following steps to create a pipeline that moves data from a source data store to a sink data store:

  1. Create linked services to link input and output data stores to your data factory.
  2. Create datasets to represent input and output data for the copy operation.
  3. Create a pipeline with a copy activity that takes a dataset as an input and a dataset as an output.

When you use the wizard, JSON definitions for these Data Factory entities (linked services, datasets, and the pipeline) are automatically created for you. When you use tools/APIs (except .NET API), you define these Data Factory entities by using the JSON format. For a sample with JSON definitions for Data Factory entities that are used to copy data from an on-premises PostgreSQL data store, see JSON example: Copy data from PostgreSQL to Azure Blob section of this article.

The following sections provide details about JSON properties that are used to define Data Factory entities specific to a PostgreSQL data store:

Linked service properties

The following table provides description for JSON elements specific to PostgreSQL linked service.

Property Description Required
type The type property must be set to: OnPremisesPostgreSql Yes
server Name of the PostgreSQL server. Yes
database Name of the PostgreSQL database. Yes
schema Name of the schema in the database. The schema name is case-sensitive. No
authenticationType Type of authentication used to connect to the PostgreSQL database. Possible values are: Anonymous, Basic, and Windows. Yes
username Specify user name if you are using Basic or Windows authentication. No
password Specify password for the user account you specified for the username. No
gatewayName Name of the gateway that the Data Factory service should use to connect to the on-premises PostgreSQL database. Yes

Dataset properties

For a full list of sections & properties available for defining datasets, see the Creating datasets article. Sections such as structure, availability, and policy of a dataset JSON are similar for all dataset types.

The typeProperties section is different for each type of dataset and provides information about the location of the data in the data store. The typeProperties section for dataset of type RelationalTable (which includes PostgreSQL dataset) has the following properties:

Property Description Required
tableName Name of the table in the PostgreSQL Database instance that linked service refers to. The tableName is case-sensitive. No (if query of RelationalSource is specified)

Copy activity properties

For a full list of sections & properties available for defining activities, see the Creating Pipelines article. Properties such as name, description, input and output tables, and policy are available for all types of activities.

Whereas, properties available in the typeProperties section of the activity vary with each activity type. For Copy activity, they vary depending on the types of sources and sinks.

When source is of type RelationalSource (which includes PostgreSQL), the following properties are available in typeProperties section:

Property Description Allowed values Required
query Use the custom query to read data. SQL query string. For example: "query": "select * from \"MySchema\".\"MyTable\"". No (if tableName of dataset is specified)

Note

Schema and table names are case-sensitive. Enclose them in "" (double quotes) in the query.

Example:

"query": "select * from \"MySchema\".\"MyTable\""

JSON example: Copy data from PostgreSQL to Azure Blob

This example provides sample JSON definitions that you can use to create a pipeline by using Visual Studio or Azure PowerShell. They show how to copy data from PostgreSQL database to Azure Blob Storage. However, data can be copied to any of the sinks stated here using the Copy Activity in Azure Data Factory.

Important

This sample provides JSON snippets. It does not include step-by-step instructions for creating the data factory. See moving data between on-premises locations and cloud article for step-by-step instructions.

The sample has the following data factory entities:

  1. A linked service of type OnPremisesPostgreSql.
  2. A linked service of type AzureStorage.
  3. An input dataset of type RelationalTable.
  4. An output dataset of type AzureBlob.
  5. The pipeline with Copy Activity that uses RelationalSource and BlobSink.

The sample copies data from a query result in PostgreSQL database to a blob every hour. The JSON properties used in these samples are described in sections following the samples.

As a first step, set up the data management gateway. The instructions are in the moving data between on-premises locations and cloud article.

PostgreSQL linked service:

{
    "name": "OnPremPostgreSqlLinkedService",
    "properties": {
        "type": "OnPremisesPostgreSql",
        "typeProperties": {
            "server": "<server>",
            "database": "<database>",
            "schema": "<schema>",
            "authenticationType": "<authentication type>",
            "username": "<username>",
            "password": "<password>",
            "gatewayName": "<gatewayName>"
        }
    }
}

Azure Blob storage linked service:

{
    "name": "AzureStorageLinkedService",
    "properties": {
        "type": "AzureStorage",
        "typeProperties": {
            "connectionString": "DefaultEndpointsProtocol=https;AccountName=<AccountName>;AccountKey=<AccountKey>"
        }
    }
}

PostgreSQL input dataset:

The sample assumes you have created a table "MyTable" in PostgreSQL and it contains a column called "timestamp" for time series data.

Setting "external": true informs the Data Factory service that the dataset is external to the data factory and is not produced by an activity in the data factory.

{
    "name": "PostgreSqlDataSet",
    "properties": {
        "type": "RelationalTable",
        "linkedServiceName": "OnPremPostgreSqlLinkedService",
        "typeProperties": {},
        "availability": {
            "frequency": "Hour",
            "interval": 1
        },
        "external": true,
        "policy": {
            "externalData": {
                "retryInterval": "00:01:00",
                "retryTimeout": "00:10:00",
                "maximumRetry": 3
            }
        }
    }
}

Azure Blob output dataset:

Data is written to a new blob every hour (frequency: hour, interval: 1). The folder path and file name for the blob are dynamically evaluated based on the start time of the slice that is being processed. The folder path uses year, month, day, and hours parts of the start time.

{
    "name": "AzureBlobPostgreSqlDataSet",
    "properties": {
        "type": "AzureBlob",
        "linkedServiceName": "AzureStorageLinkedService",
        "typeProperties": {
            "folderPath": "mycontainer/postgresql/yearno={Year}/monthno={Month}/dayno={Day}/hourno={Hour}",
            "format": {
                "type": "TextFormat",
                "rowDelimiter": "\n",
                "columnDelimiter": "\t"
            },
            "partitionedBy": [
                {
                    "name": "Year",
                    "value": {
                        "type": "DateTime",
                        "date": "SliceStart",
                        "format": "yyyy"
                    }
                },
                {
                    "name": "Month",
                    "value": {
                        "type": "DateTime",
                        "date": "SliceStart",
                        "format": "MM"
                    }
                },
                {
                    "name": "Day",
                    "value": {
                        "type": "DateTime",
                        "date": "SliceStart",
                        "format": "dd"
                    }
                },
                {
                    "name": "Hour",
                    "value": {
                        "type": "DateTime",
                        "date": "SliceStart",
                        "format": "HH"
                    }
                }
            ]
        },
        "availability": {
            "frequency": "Hour",
            "interval": 1
        }
    }
}

Pipeline with Copy activity:

The pipeline contains a Copy Activity that is configured to use the input and output datasets and is scheduled to run hourly. In the pipeline JSON definition, the source type is set to RelationalSource and sink type is set to BlobSink. The SQL query specified for the query property selects the data from the public.usstates table in the PostgreSQL database.

{
    "name": "CopyPostgreSqlToBlob",
    "properties": {
        "description": "pipeline for copy activity",
        "activities": [
            {
                "type": "Copy",
                "typeProperties": {
                    "source": {
                        "type": "RelationalSource",
                        "query": "select * from \"public\".\"usstates\""
                    },
                    "sink": {
                        "type": "BlobSink"
                    }
                },
                "inputs": [
                    {
                        "name": "PostgreSqlDataSet"
                    }
                ],
                "outputs": [
                    {
                        "name": "AzureBlobPostgreSqlDataSet"
                    }
                ],
                "policy": {
                    "timeout": "01:00:00",
                    "concurrency": 1
                },
                "scheduler": {
                    "frequency": "Hour",
                    "interval": 1
                },
                "name": "PostgreSqlToBlob"
            }
        ],
        "start": "2014-06-01T18:00:00Z",
        "end": "2014-06-01T19:00:00Z"
    }
}

Type mapping for PostgreSQL

As mentioned in the data movement activities article Copy activity performs automatic type conversions from source types to sink types with the following 2-step approach:

  1. Convert from native source types to .NET type
  2. Convert from .NET type to native sink type

When moving data to PostgreSQL, the following mappings are used from PostgreSQL type to .NET type.

PostgreSQL Database type PostgresSQL aliases .NET Framework type
abstime Datetime
bigint int8 Int64
bigserial serial8 Int64
bit [(n)] Byte[], String
bit varying [ (n) ] varbit Byte[], String
boolean bool Boolean
box Byte[], String
bytea Byte[], String
character [(n)] char [(n)] String
character varying [(n)] varchar [(n)] String
cid String
cidr String
circle Byte[], String
date Datetime
daterange String
double precision float8 Double
inet Byte[], String
intarry String
int4range String
int8range String
integer int, int4 Int32
interval [fields] [(p)] Timespan
json String
jsonb Byte[]
line Byte[], String
lseg Byte[], String
macaddr Byte[], String
money Decimal
numeric [(p, s)] decimal [(p, s)] Decimal
numrange String
oid Int32
path Byte[], String
pg_lsn Int64
point Byte[], String
polygon Byte[], String
real float4 Single
smallint int2 Int16
smallserial serial2 Int16
serial serial4 Int32
text String

Map source to sink columns

To learn about mapping columns in source dataset to columns in sink dataset, see Mapping dataset columns in Azure Data Factory.

Repeatable read from relational sources

When copying data from relational data stores, keep repeatability in mind to avoid unintended outcomes. In Azure Data Factory, you can rerun a slice manually. You can also configure retry policy for a dataset so that a slice is rerun when a failure occurs. When a slice is rerun in either way, you need to make sure that the same data is read no matter how many times a slice is run. See Repeatable read from relational sources.

Performance and Tuning

See Copy Activity Performance & Tuning Guide to learn about key factors that impact performance of data movement (Copy Activity) in Azure Data Factory and various ways to optimize it.