PostgreSQL output connector

Experimental feature

PostgreSQL support is an experimental feature of Feldera.

Feldera allows you to output data from a SQL view to a PostgreSQL database. These outputs are made as a series of transactions.

important

Only SQL views with Uniqueness Constraints can output data to a PostgreSQL table.

It is recommended that these unique keys in Feldera be defined as PRIMARY KEYs in the PostgreSQL table.

PostgreSQL output configuration

Property	Type	Default	Description
`uri`*	string		A PostgreSQL connection URL, e.g., "postgresql://postgres:1234@127.0.0.1:7373/postgres" (see the tokio-postgres Config struct for a detailed list of options)
`table`*	string		The PostgreSQL table to write the outputs to. The schema of this table should be compatible with the schema of the output view.

[*]: Required fields

The schema of the PostgreSQL table should match the schema of the Feldera view, as outlined in the table below, with the following exceptions::

Narrower Feldera types such as INT2 and FLOAT4 can be stored in wider PostgreSQL column types like INT8 and FLOAT8 respectively.
Columns in the PostgreSQL table that are nullable or have default values may be omitted from the Feldera view.

Connecting with TLS / SSL

Currently, we do not support connecting to PostgreSQL with TLS / SSL. Please let us know if you need TLS / SSL support.

Data type mapping

info

The following table lists supported PostgreSQL data types. Please let us know if you need support for a specific type.

Feldera Type	PostgreSQL Type	Comments
BOOL	BOOL
TINYINT	SMALLINT, INT, BIGINT	No direct equivalent but can be stored in wider types.
SMALLINT	SMALLINT
INT	INT
BIGINT	BIGINT
DECIMAL	DECIMAL
REAL	FLOAT
DOUBLE	DOUBLE PRECISION
VARCHAR	VARCHAR
TIME	TIME
DATE	DATE
TIMESTAMP	TIMESTAMP	Feldera TIMESTAMPs do not have timezone information.
VARIANT	JSON, JSONB
UUID	UUID
VARBINARY	BYTEA
ARRAY	ARRAY
User Defined Type	Equivalent PostgreSQL Composite Type
MAP	JSON, JSONB	No direct equivalent type, but can be stored as JSON / JSONB.

Example

Let's first connect to a PostgreSQL database running in localhost:5432 with the username postgres and password password.

psql postgres://postgres:password@localhost:5432/postgres

Now, let's create a table feldera_out with columns id and s that Feldera will write to.

-- PostgreSQL
CREATE TABLE feldera_out (id INT PRIMARY KEY, s VARCHAR);

We can output data from Feldera to this table using the postgres_output connector.

-- Feldera SQL
-- Create a table and fill it with 5 randomly generated records.
create table t0 (id int, s varchar) with (
  'connectors' = '[{
    "transport": {
      "name": "datagen",
      "config": {
        "plan": [{
          "rate": 1,
          "limit": 5
        }]
      }
    }
  }]'
);

-- Create a view that will contain a copy of all records in table `t0` and
-- attach a Postgres output connector to it.
create materialized view v1 with (
    'connectors' = '[{
        "index": "v1_idx",
        "transport": {
            "name": "postgres_output",
            "config": {
                "uri": "postgres://postgres:password@localhost:5432/postgres",
                "table": "feldera_out"
            }
        }
    }]'
) as select * from t0;

-- Index `v1` using `id` column as a key. The Postgres connector requires this
-- index to group updates by key.
create index v1_idx on v1(id);

important

Column names in Feldera SQL view and PostgreSQL table need to match.

Example demonstrating all supported types

We create a Feldera pipeline including every supported PostgreSQL type:

-- Feldera SQL
CREATE TABLE all_types_example (
    my_bool              BOOL,
    my_bool_array        BOOL ARRAY,
    my_bytea             BYTEA,
    my_char              CHAR,
    my_char_array        CHAR ARRAY,
    my_varchar           VARCHAR(50),
    my_varchar_array     VARCHAR(50) ARRAY,
    my_name              STRING,
    my_name_array        STRING ARRAY,
    my_date              DATE,
    my_date_array        DATE ARRAY,
    my_time              TIME,
    my_time_array        TIME ARRAY,
    my_timestamp         TIMESTAMP,
    my_timestamp_array   TIMESTAMP ARRAY,
    my_int2              INT2,
    my_int2_array        INT2 ARRAY,
    my_int4              INT4,
    my_int4_array        INT4 ARRAY,
    my_int8              INT8,
    my_int8_array        INT8 ARRAY,
    my_float4            FLOAT4,
    my_float4_array      FLOAT4 ARRAY,
    my_float8            FLOAT8,
    my_float8_array      FLOAT8 ARRAY,
    my_text              TEXT,
    my_text_array        TEXT ARRAY,
    my_uuid              UUID,
    my_uuid_array        UUID ARRAY,
    my_json              VARIANT,
    my_decimal           DECIMAL(28, 2),
    my_decimal_array     DECIMAL(28, 2) ARRAY
) WITH (
  'connectors' = '[{
    "transport": {
      "name": "datagen",
      "config": {
        "plan": [{
          "rate": 1,
          "limit": 5
        }]
      }
    }
  }]'
);

create materialized view v1
with (
    'connectors' = '[{
        "index": "v1_idx",
        "transport": {
            "name": "postgres_output",
            "config": {
                "uri": "postgres://postgres:password@localhost:5432/postgres",
                "table": "all_types_example"
            }
        }
    }]'
)
as select * from all_types_example;
create index v1_idx on v1(my_int2);

Now we create the equivalent table in PostgreSQL.

-- PostgreSQL
CREATE TABLE all_types_example (
    my_bool              BOOL,
    my_bool_array        BOOL[],
    my_bytea             BYTEA,
    my_char              CHAR,
    my_char_array        CHAR[],
    my_varchar           VARCHAR(50),
    my_varchar_array     VARCHAR(50)[],
    my_name              NAME,
    my_name_array        NAME[],
    my_date              DATE,
    my_date_array        DATE[],
    my_time              TIME,
    my_time_array        TIME[],
    my_timestamp         TIMESTAMP,
    my_timestamp_array   TIMESTAMP[],
    my_int2              INT2,
    my_int2_array        INT2[],
    my_int4              INT4,
    my_int4_array        INT4[],
    my_int8              INT8,
    my_int8_array        INT8[],
    my_float4            FLOAT4,
    my_float4_array      FLOAT4[],
    my_float8            FLOAT8,
    my_float8_array      FLOAT8[],
    my_text              TEXT,
    my_text_array        TEXT[],
    my_uuid              UUID,
    my_uuid_array        UUID[],
    my_json              JSON,
    my_decimal           DECIMAL(28, 2),
    my_decimal_array     DECIMAL(28, 2)[]
);

After the pipeline completes, we can inspect the PostgreSQL table to ensure that Feldera has written to it:

-- PostgreSQL
SELECT count(*) FROM all_types_example;
 count
-------
     5
(1 row)

Outputing multi-dimensional arrays

When working with multi-dementional arrays, like BYTEA ARRAY, PostgreSQL expects the sub arrays to be of the same length. If not, the following error is raised:

Multidimensional arrays must have sub-arrays with matching dimensions.

danger

If the sub-arrays are not of the same dimension, the transaction will fail and the data will be lost.

PostgreSQL output configuration​

Connecting with TLS / SSL​

Data type mapping​

Example​

Example demonstrating all supported types​

Outputing multi-dimensional arrays​