Kafka connector#

Overview#

This connector allows the use of Apache Kafka topics as tables in Trino. Each message is presented as a row in Trino.

Topics can be live. Rows appear as data arrives, and disappear as segments get dropped. This can result in strange behavior if accessing the same table multiple times in a single query (e.g., performing a self join).

The connector reads and writes message data from Kafka topics in parallel across workers to achieve a significant performance gain. The size of data sets for this parallelization is configurable and can therefore be adapted to your specific needs.

See the Kafka Connector Tutorial.

Note

The minimum supported Kafka broker version is 0.10.0. The connector is tested against Confluent Kafka versions 5.2.1 and 5.5.2, but any intermediate or newer versions are expected to work.

Configuration#

To configure the Kafka connector, create a catalog properties file etc/catalog/kafka.properties with the following contents, replacing the properties as appropriate:

connector.name=kafka
kafka.table-names=table1,table2
kafka.nodes=host1:port,host2:port

Multiple Kafka clusters#

You can have as many catalogs as you need, so if you have additional Kafka clusters, simply add another properties file to etc/catalog with a different name (making sure it ends in .properties). For example, if you name the property file sales.properties, Trino creates a catalog named sales using the configured connector.

Configuration properties#

The following configuration properties are available:

Property Name

Description

kafka.default-schema

Default schema name for tables

kafka.nodes

List of nodes in the Kafka cluster

kafka.buffer-size

Kafka read buffer size

kafka.hide-internal-columns

Controls whether internal columns are part of the table schema or not

kafka.messages-per-split

Number of messages that are processed by each Trino split, defaults to 100000

kafka.timestamp-upper-bound-force-push-down-enabled

Controls if upper bound timestamp push down is enabled for topics using CreateTime mode

In addition, you need to configure table schema and schema registry usage with the relevant properties.

kafka.default-schema#

Defines the schema which contains all tables that were defined without a qualifying schema name.

This property is optional; the default is default.

kafka.nodes#

A comma separated list of hostname:port pairs for the Kafka data nodes.

This property is required; there is no default and at least one node must be defined.

Note

Trino must still be able to connect to all nodes of the cluster even if only a subset is specified here as segment files may be located only on a specific node.

kafka.buffer-size#

Size of the internal data buffer for reading data from Kafka. The data buffer must be able to hold at least one message and ideally can hold many messages. There is one data buffer allocated per worker and data node.

This property is optional; the default is 64kb.

kafka.timestamp-upper-bound-force-push-down-enabled#

The upper bound predicate on _timestamp column is pushed down only for topics using LogAppendTime mode.

For topics using CreateTime mode, upper bound push down must be explicitly allowed via kafka.timestamp-upper-bound-force-push-down-enabled config property or timestamp_upper_bound_force_push_down_enabled session property.

This property is optional; the default is false.

kafka.hide-internal-columns#

In addition to the data columns defined in a table description file, the connector maintains a number of additional columns for each table. If these columns are hidden, they can still be used in queries but do not show up in DESCRIBE <table-name> or SELECT *.

This property is optional; the default is true.

Internal columns#

For each defined table, the connector maintains the following columns:

Column name

Type

Description

_partition_id

BIGINT

ID of the Kafka partition which contains this row.

_partition_offset

BIGINT

Offset within the Kafka partition for this row.

_segment_start

BIGINT

Lowest offset in the segment (inclusive) which contains this row. This offset is partition specific.

_segment_end

BIGINT

Highest offset in the segment (exclusive) which contains this row. The offset is partition specific. This is the same value as _segment_start of the next segment (if it exists).

_segment_count

BIGINT

Running count for the current row within the segment. For an uncompacted topic, _segment_start + _segment_count is equal to _partition_offset.

_message_corrupt

BOOLEAN

True if the decoder could not decode the message for this row. When true, data columns mapped from the message should be treated as invalid.

_message

VARCHAR

Message bytes as an UTF-8 encoded string. This is only useful for a text topic.

_message_length

BIGINT

Number of bytes in the message.

_headers

map(VARCHAR, array(VARBINARY))

Headers of the message where values with the same key are grouped as array.

_key_corrupt

BOOLEAN

True if the key decoder could not decode the key for this row. When true, data columns mapped from the key should be treated as invalid.

_key

VARCHAR

Key bytes as an UTF-8 encoded string. This is only useful for textual keys.

_key_length

BIGINT

Number of bytes in the key.

_timestamp

TIMESTAMP

Message timestamp.

For tables without a table definition file, the _key_corrupt and _message_corrupt columns will always be false.

Table schema and schema registry usage#

The table schema for the messages can be supplied to the connector with a configuration file or a schema registry. It also provides a mechanism for the connector to discover tables.

You have to configure the supplier with the kafka.table-description-supplier property, setting it to FILE or CONFLUENT. Each table description supplier has a separate set of configuration properties.

Refer to the following subsections for more detail. The FILE table description supplier is the default and the value is case insensitive.

File table description supplier#

In order to use the file based table description supplier, the kafka.table-description-supplier must be set to FILE which is the default.

In addition, you must set kafka.table-names and kafka.table-description-dir as described in the following sections:

kafka.table-names#

Comma-separated list of all tables provided by this catalog. A table name can be unqualified (simple name), and is then placed into the default schema (see below), or it can be qualified with a schema name (<schema-name>.<table-name>).

For each table defined here, a table description file (see below) may exist. If no table description file exists, the table name is used as the topic name on Kafka and no data columns are mapped into the table. The table still contains all internal columns (see below).

This property is required; there is no default and at least one table must be defined.

kafka.table-description-dir#

References a folder within Trino deployment that holds one or more JSON files (must end with .json) which contain table description files.

This property is optional; the default is etc/kafka.

Table definition files#

Kafka maintains topics only as byte messages and leaves it to producers and consumers to define how a message should be interpreted. For Trino, this data must be mapped into columns to allow queries against the data.

Note

For textual topics that contain JSON data, it is entirely possible to not use any table definition files, but instead use the Trino JSON Functions and Operators to parse the _message column which contains the bytes mapped into an UTF-8 string. This is, however, pretty cumbersome and makes it difficult to write SQL queries. This only works when reading data.

A table definition file consists of a JSON definition for a table. The name of the file can be arbitrary but must end in .json. Place the file in the directory configured with the kafka.table-description-dir property. The table definition file must be accessible from all Trino nodes.

{
    "tableName": ...,
    "schemaName": ...,
    "topicName": ...,
    "key": {
        "dataFormat": ...,
        "fields": [
            ...
        ]
    },
    "message": {
        "dataFormat": ...,
        "fields": [
            ...
       ]
    }
}

Field

Required

Type

Description

tableName

required

string

Trino table name defined by this file.

schemaName

optional

string

Schema which will contain the table. If omitted, the default schema name is used.

topicName

required

string

Kafka topic that is mapped.

key

optional

JSON object

Field definitions for data columns mapped to the message key.

message

optional

JSON object

Field definitions for data columns mapped to the message itself.

Key and message in Kafka#

Starting with Kafka 0.8, each message in a topic can have an optional key. A table definition file contains sections for both key and message to map the data onto table columns.

Each of the key and message fields in the table definition is a JSON object that must contain two fields:

Field

Required

Type

Description

dataFormat

required

string

Selects the decoder for this group of fields.

fields

required

JSON array

A list of field definitions. Each field definition creates a new column in the Trino table.

Each field definition is a JSON object:

{
    "name": ...,
    "type": ...,
    "dataFormat": ...,
    "mapping": ...,
    "formatHint": ...,
    "hidden": ...,
    "comment": ...
}

Field

Required

Type

Description

name

required

string

Name of the column in the Trino table.

type

required

string

Trino type of the column.

dataFormat

optional

string

Selects the column decoder for this field. Defaults to the default decoder for this row data format and column type.

dataSchema

optional

string

The path or URL where the Avro schema resides. Used only for Avro decoder.

mapping

optional

string

Mapping information for the column. This is decoder specific, see below.

formatHint

optional

string

Sets a column specific format hint to the column decoder.

hidden

optional

boolean

Hides the column from DESCRIBE <table name> and SELECT *. Defaults to false.

comment

optional

string

Adds a column comment which is shown with DESCRIBE <table name>.

There is no limit on field descriptions for either key or message.

Confluent table description supplier#

The Confluent table description supplier uses the Confluent Schema Registry to discover table definitions. Set kafka.table-description-supplier to CONFLUENT to use it and configure the additional properties in the following table.

The benefits of using the schema registry is that new tables can be defined without a cluster restart, schema updates are detected and there is no need to define tables manually.

Note

Inserts are not supported and the only data format supported is AVRO.

Confluent table description supplier properties#

Property name

Description

Default value

kafka.confluent-schema-registry-url

Comma-separated list of URL addresses for the Confluent schema registry. For example, http://schema-registry-1.example.org:8081,http://schema-registry-2.example.org:8081

kafka.confluent-schema-registry-client-cache-size

The maximum number of subjects that can be stored in the local cache. The cache stores the schemas locally by subjectId, and is provided by the Confluent CachingSchemaRegistry client.

1000

kafka.empty-field-strategy

Avro allows empty struct fields but this is not allowed in Trino. There are three strategies for handling empty struct fields:

  • IGNORE, ignore structs with no fields. This propagate to parents. For example an array of structs with no fields is ignored.

  • FAIL, fail the query if a struct with no fields is defined.

  • DUMMY, add a dummy boolean field called dummy which is null. This may be desired if the struct represents a marker field.

IGNORE

kafka.confluent-subjects-cache-refresh-interval

The interval used for refreshing the list of subjects and the definition of the schema for the subject in the subjects cache

1s

Confluent subject to table name mapping#

The subject naming strategy determines how a subject is resolved from the table name.

The default strategy is the TopicNameStrategy where the key subject is defined as <topic-name>-key and the value subject is defined as <topic-name>-value. If other strategies are used there is no way to determine the subject name beforehand so it must be specified manually in the table name.

To manually specify the key and value subjects just append to the table name, for example: <topic name>&key-subject=<key subject>&value-subject=<value subject. Both the key-subject and value-subject parameters are optional. If either is not specified then the default TopicNameStrategy is used to resolve the subject name via the topic name. Note that a case insensitive match must be done, as identifiers cannot contain upper case characters.

Kafka inserts#

The Kafka connector supports the use of INSERT statements to write data to a Kafka topic. Table column data is mapped to Kafka messages as defined in the table definition file. There are four supported data formats for key and message encoding:

These data formats each have an encoder that maps column values into bytes to be sent to a Kafka topic.

Trino supports at-least-once delivery for Kafka producers. This means that messages are guaranteed to be sent to Kafka topics at least once. If a producer acknowledgement times out or if the producer receives an error, it might retry sending the message. This could result in a duplicate message being sent to the Kafka topic.

The Kafka connector does not allow the user to define which partition will be used as the target for a message. If a message includes a key, the producer will use a hash algorithm to choose the target partition for the message. The same key will always be assigned the same partition.

Row encoding#

Encoding is required to allow writing data and defines how table columns in Trino map to Kafka keys and message data.

The Kafka connector contains the following encoders:

  • raw encoder - Table columns are mapped to a Kafka message as raw bytes

  • CSV encoder - Kafka message is formatted as comma separated value

  • JSON encoder - Table columns are mapped to JSON fields

  • Avro encoder - Table columns are mapped to Avro fields based on an Avro schema

Note

A table definition file must be defined for the encoder to work.

Raw encoder#

The raw encoder formats the table columns as raw bytes using the mapping information specified in the table definition file.

The following field attributes are supported:

  • dataFormat - Specifies the width of the column data type

  • type - Trino data type

  • mapping - start and optional end position of bytes to convert (specified as start or start:end)

The dataFormat attribute selects the number of bytes converted. If absent, BYTE is assumed. All values are signed.

Supported values:

  • BYTE - one byte

  • SHORT - two bytes (big-endian)

  • INT - four bytes (big-endian)

  • LONG - eight bytes (big-endian)

  • FLOAT - four bytes (IEEE 754 format, big-endian)

  • DOUBLE - eight bytes (IEEE 754 format, big-endian)

The type attribute defines the Trino data type.

Different values of dataFormat are supported, depending on the Trino data type:

Trino data type

dataFormat values

BIGINT

BYTE, SHORT, INT, LONG

INTEGER

BYTE, SHORT, INT

SMALLINT

BYTE, SHORT

TINYINT

BYTE

REAL

FLOAT

DOUBLE

FLOAT, DOUBLE

BOOLEAN

BYTE, SHORT, INT, LONG

VARCHAR / VARCHAR(x)

BYTE

The mapping attribute specifies the range of bytes in a key or message used for encoding.

Note

Both a start and end position must be defined for VARCHAR types. Otherwise, there is no way to know how many bytes the message contains. The raw format mapping information is static and cannot be dynamically changed to fit the variable width of some Trino data types.

If only a start position is given:

  • For fixed width types, the appropriate number of bytes are used for the specified dateFormat (see above).

If both a start and end position are given, then:

  • For fixed width types, the size must be equal to number of bytes used by specified dataFormat.

  • All bytes between start (inclusive) and end (exclusive) are used.

Note

All mappings must include a start position for encoding to work.

The encoding for numeric data types (BIGINT, INTEGER, SMALLINT, TINYINT, REAL, DOUBLE) is straightforward. All numeric types use big-endian. Floating point types use IEEE 754 format.

Example raw field definition in a table definition file for a Kafka message:

{
  "tableName": "your-table-name",
  "schemaName": "your-schema-name",
  "topicName": "your-topic-name",
  "key": { "..." },
  "message": {
    "dataFormat": "raw",
    "fields": [
      {
        "name": "field1",
        "type": "BIGINT",
        "dataFormat": "LONG",
        "mapping": "0"
      },
      {
        "name": "field2",
        "type": "INTEGER",
        "dataFormat": "INT",
        "mapping": "8"
      },
      {
        "name": "field3",
        "type": "SMALLINT",
        "dataFormat": "LONG",
        "mapping": "12"
      },
      {
        "name": "field4",
        "type": "VARCHAR(6)",
        "dataFormat": "BYTE",
        "mapping": "20:26"
      }
    ]
  }
}

Columns should be defined in the same order they are mapped. There can be no gaps or overlaps between column mappings. The width of the column as defined by the column mapping must be equivalent to the width of the dataFormat for all types except for variable width types.

Example insert query for the above table definition:

INSERT INTO example_raw_table (field1, field2, field3, field4)
  VALUES (123456789, 123456, 1234, 'abcdef');

When inserting variable width types, the value must be exactly equal to the width defined in the table definition file. If the inserted value is longer it gets truncated, resulting in data loss. If the inserted value is shorter the decoder will not be able to properly read the value because there is no defined padding character. Due to these constraints the encoder fails if the width of the inserted value is not equal to the mapping width.

CSV encoder#

The CSV encoder formats the values for each row as a line of comma-separated-values (CSV) using UTF-8 encoding. The CSV line is formatted with a comma ‘,’ as the column delimiter.

The type and mapping attributes must be defined for each field:

  • type - Trino data type

  • mapping - The integer index of the column in the CSV line (the first column is 0, the second is 1, and so on)

dataFormat and formatHint are not supported and must be omitted.

The following Trino data types are supported by the CSV encoder:

  • BIGINT

  • INTEGER

  • SMALLINT

  • TINYINT

  • DOUBLE

  • REAL

  • BOOLEAN

  • VARCHAR / VARCHAR(x)

Column values are converted to strings before they are formatted as a CSV line.

Example CSV field definition in a table definition file for a Kafka message:

{
  "tableName": "your-table-name",
  "schemaName": "your-schema-name",
  "topicName": "your-topic-name",
  "key": { "..." },
  "message": {
    "dataFormat": "csv",
    "fields": [
      {
        "name": "field1",
        "type": "BIGINT",
        "mapping": "0"
      },
      {
        "name": "field2",
        "type": "VARCHAR",
        "mapping": "1"
      },
      {
        "name": "field3",
        "type": "BOOLEAN",
        "mapping": "2"
      }
    ]
  }
}

Example insert query for the above table definition:

INSERT INTO example_csv_table (field1, field2, field3)
  VALUES (123456789, 'example text', TRUE);

JSON encoder#

The JSON encoder maps table columns to JSON fields defined in the table definition file according to RFC 4627.

For fields, the following attributes are supported:

  • type - Trino type of column.

  • mapping - slash-separated list of field names to select a field from the JSON object

  • dataFormat - name of formatter (required for temporal types)

  • formatHint - pattern to format temporal data (only use with custom-date-time formatter)

The following Trino data types are supported by the JSON encoder:

Trino data types

BIGINT

INTEGER

SMALLINT

TINYINT

DOUBLE

REAL

BOOLEAN

VARCHAR

DATE

TIME

TIME WITH TIME ZONE

TIMESTAMP

TIMESTAMP WITH TIME ZONE

The following dataFormats are available for temporal data:

  • iso8601

  • rfc2822

  • custom-date-time - formats temporal data according to Joda Time pattern given by formatHint field

  • milliseconds-since-epoch

  • seconds-since-epoch

All temporal data in Kafka supports milliseconds precision

The following table defines which temporal data types are supported by dataFormats:

Trino data type

Decoding rules

DATE

custom-date-time, iso8601

TIME

custom-date-time, iso8601, milliseconds-since-epoch, seconds-since-epoch

TIME WITH TIME ZONE

custom-date-time, iso8601

TIMESTAMP

custom-date-time, iso8601, rfc2822, milliseconds-since-epoch, seconds-since-epoch

TIMESTAMP WITH TIME ZONE

custom-date-time, iso8601, rfc2822, milliseconds-since-epoch, seconds-since-epoch

Example JSON field definition in a table definition file for a Kafka message:

{
  "tableName": "your-table-name",
  "schemaName": "your-schema-name",
  "topicName": "your-topic-name",
  "key": { "..." },
  "message": {
    "dataFormat": "json",
    "fields": [
      {
        "name": "field1",
        "type": "BIGINT",
        "mapping": "field1"
      },
      {
        "name": "field2",
        "type": "VARCHAR",
        "mapping": "field2"
      },
      {
        "name": "field3",
        "type": "TIMESTAMP",
        "dataFormat": "custom-date-time",
        "formatHint": "yyyy-dd-MM HH:mm:ss.SSS",
        "mapping": "field3"
      }
    ]
  }
}

Example insert query for the above table definition:

INSERT INTO example_json_table (field1, field2, field3)
  VALUES (123456789, 'example text', TIMESTAMP '2020-07-15 01:02:03.456');

Avro encoder#

The Avro encoder serializes rows to Avro records as defined by the Avro schema. Trino does not support schema-less Avro encoding.

Note

The Avro schema is encoded with the table column values in each Kafka message

The dataSchema must be defined in the table definition file to use the Avro encoder. It points to the location of the Avro schema file for the key or message

Avro schema files can be retrieved via HTTP or HTTPS from remote server with the syntax:

"dataSchema": "http://example.org/schema/avro_data.avsc"

Local files need to be available on all Trino nodes and use an absolute path in the syntax, for example:

"dataSchema": "/usr/local/schema/avro_data.avsc"

The following field attributes are supported:

  • name - Name of the column in the Trino table.

  • type - Trino type of column.

  • mapping - slash-separated list of field names to select a field from the Avro schema. If the field specified in mapping does not exist in the original Avro schema, then a write operation fails.

The following table lists supported Trino types, which can be used in type for the equivalent Avro field type.

Trino data type

Avro data type

BIGINT

INT, LONG

REAL

FLOAT

DOUBLE

FLOAT, DOUBLE

BOOLEAN

BOOLEAN

VARCHAR / VARCHAR(x)

STRING

Example Avro field definition in a table definition file for a Kafka message:

{
  "tableName": "your-table-name",
  "schemaName": "your-schema-name",
  "topicName": "your-topic-name",
  "key": { "..." },
  "message":
  {
    "dataFormat": "avro",
    "dataSchema": "/avro_message_schema.avsc",
    "fields":
    [
      {
        "name": "field1",
        "type": "BIGINT",
        "mapping": "field1"
      },
      {
        "name": "field2",
        "type": "VARCHAR",
        "mapping": "field2"
      },
      {
        "name": "field3",
        "type": "BOOLEAN",
        "mapping": "field3"
      }
    ]
  }
}

Example Avro schema definition for the above table definition:

{
  "type" : "record",
  "name" : "example_avro_message",
  "namespace" : "io.trino.plugin.kafka",
  "fields" :
  [
    {
      "name":"field1",
      "type":["null", "long"],
      "default": null
    },
    {
      "name": "field2",
      "type":["null", "string"],
      "default": null
    },
    {
      "name":"field3",
      "type":["null", "boolean"],
      "default": null
    }
  ],
  "doc:" : "A basic avro schema"
}

Example insert query for the above table definition:

INSERT INTO example_avro_table (field1, field2, field3)
  VALUES (123456789, 'example text', FALSE);

Row decoding#

For key and message, a decoder is used to map message and key data onto table columns.

The Kafka connector contains the following decoders:

  • raw - Kafka message is not interpreted, ranges of raw message bytes are mapped to table columns

  • csv - Kafka message is interpreted as comma separated message, and fields are mapped to table columns

  • json - Kafka message is parsed as JSON and JSON fields are mapped to table columns

  • avro - Kafka message is parsed based on an Avro schema and Avro fields are mapped to table columns

Note

If no table definition file exists for a table, the dummy decoder is used, which does not expose any columns.

raw decoder#

The raw decoder supports reading of raw (byte-based) values from Kafka message or key and converting it into Trino columns.

For fields, the following attributes are supported:

  • dataFormat - selects the width of the data type converted

  • type - Trino data type (see table below for list of supported data types)

  • mapping - <start>[:<end>]; start and end position of bytes to convert (optional)

The dataFormat attribute selects the number of bytes converted. If absent, BYTE is assumed. All values are signed.

Supported values are:

  • BYTE - one byte

  • SHORT - two bytes (big-endian)

  • INT - four bytes (big-endian)

  • LONG - eight bytes (big-endian)

  • FLOAT - four bytes (IEEE 754 format)

  • DOUBLE - eight bytes (IEEE 754 format)

The type attribute defines the Trino data type on which the value is mapped.

Depending on Trino type assigned to column different values of dataFormat can be used:

Trino data type

Allowed dataFormat values

BIGINT

BYTE, SHORT, INT, LONG

INTEGER

BYTE, SHORT, INT

SMALLINT

BYTE, SHORT

TINYINT

BYTE

DOUBLE

DOUBLE, FLOAT

BOOLEAN

BYTE, SHORT, INT, LONG

VARCHAR / VARCHAR(x)

BYTE

The mapping attribute specifies the range of the bytes in a key or message used for decoding. It can be one or two numbers separated by a colon (<start>[:<end>]).

If only a start position is given:

  • For fixed width types the column will use the appropriate number of bytes for the specified dateFormat (see above).

  • When VARCHAR value is decoded all bytes from start position till the end of the message will be used.

If start and end position are given, then:

  • For fixed width types the size must be equal to number of bytes used by specified dataFormat.

  • For VARCHAR all bytes between start (inclusive) and end (exclusive) are used.

If no mapping attribute is specified, it is equivalent to setting start position to 0 and leaving end position undefined.

Decoding scheme of numeric data types (BIGINT, INTEGER, SMALLINT, TINYINT, DOUBLE) is straightforward. A sequence of bytes is read from input message and decoded according to either:

  • big-endian encoding (for integer types)

  • IEEE 754 format for (for DOUBLE).

Length of decoded byte sequence is implied by the dataFormat.

For VARCHAR data type a sequence of bytes is interpreted according to UTF-8 encoding.

csv decoder#

The CSV decoder converts the bytes representing a message or key into a string using UTF-8 encoding and then interprets the result as a CSV (comma-separated value) line.

For fields, the type and mapping attributes must be defined:

  • type - Trino data type (see table below for list of supported data types)

  • mapping - the index of the field in the CSV record

dataFormat and formatHint are not supported and must be omitted.

Table below lists supported Trino types, which can be used in type and decoding scheme:

Trino data type

Decoding rules

BIGINT
INTEGER
SMALLINT
TINYINT

Decoded using Java Long.parseLong()

DOUBLE

Decoded using Java Double.parseDouble()

BOOLEAN

“true” character sequence maps to true; Other character sequences map to false

VARCHAR / VARCHAR(x)

Used as is

json decoder#

The JSON decoder converts the bytes representing a message or key into a JSON according to RFC 4627. Note that the message or key MUST convert into a JSON object, not an array or simple type.

For fields, the following attributes are supported:

  • type - Trino type of column.

  • dataFormat - Field decoder to be used for column.

  • mapping - slash-separated list of field names to select a field from the JSON object

  • formatHint - only for custom-date-time, see below

The JSON decoder supports multiple field decoders, with _default being used for standard table columns and a number of decoders for date and time based types.

The table below lists Trino data types, which can be used as in type, and matching field decoders, which can be specified via dataFormat attribute.

Trino data type

Allowed dataFormat values

BIGINT
INTEGER
SMALLINT
TINYINT
DOUBLE
BOOLEAN
VARCHAR
VARCHAR(x)

Default field decoder (omitted dataFormat attribute)

DATE

custom-date-time, iso8601

TIME

custom-date-time, iso8601, milliseconds-since-epoch, seconds-since-epoch

TIME WITH TIME ZONE

custom-date-time, iso8601

TIMESTAMP

custom-date-time, iso8601, rfc2822, milliseconds-since-epoch, seconds-since-epoch

TIMESTAMP WITH TIME ZONE

custom-date-time, iso8601, rfc2822, milliseconds-since-epoch seconds-since-epoch

Default field decoder#

This is the standard field decoder, supporting all the Trino physical data types. A field value is transformed under JSON conversion rules into boolean, long, double or string values. For non-date/time based columns, this decoder should be used.

Date and time decoders#

To convert values from JSON objects into Trino DATE, TIME, TIME WITH TIME ZONE, TIMESTAMP or TIMESTAMP WITH TIME ZONE columns, special decoders must be selected using the dataFormat attribute of a field definition.

  • iso8601 - text based, parses a text field as an ISO 8601 timestamp.

  • rfc2822 - text based, parses a text field as an RFC 2822 timestamp.

  • custom-date-time - text based, parses a text field according to Joda format pattern

    specified via formatHint attribute. Format pattern should conform to https://www.joda.org/joda-time/apidocs/org/joda/time/format/DateTimeFormat.html.

  • milliseconds-since-epoch - number based, interprets a text or number as number of milliseconds since the epoch.

  • seconds-since-epoch - number based, interprets a text or number as number of milliseconds since the epoch.

For TIMESTAMP WITH TIME ZONE and TIME WITH TIME ZONE data types, if timezone information is present in decoded value, it will be used in Trino value. Otherwise result time zone will be set to UTC.

avro decoder#

The Avro decoder converts the bytes representing a message or key in Avro format based on a schema. The message must have the Avro schema embedded. Trino does not support schema-less Avro decoding.

For key/message, using avro decoder, the dataSchema must be defined. This should point to the location of a valid Avro schema file of the message which needs to be decoded. This location can be a remote web server (e.g.: dataSchema: 'http://example.org/schema/avro_data.avsc') or local file system(e.g.: dataSchema: '/usr/local/schema/avro_data.avsc'). The decoder fails if this location is not accessible from the Trino coordinator node.

For fields, the following attributes are supported:

  • name - Name of the column in the Trino table.

  • type - Trino type of column.

  • mapping - slash-separated list of field names to select a field from the Avro schema. If field specified in mapping does not exist in the original Avro schema then a read operation returns NULL.

Table below lists supported Trino types which can be used in type for the equivalent Avro field type/s.

Trino data type

Allowed Avro data type

BIGINT

INT, LONG

DOUBLE

DOUBLE, FLOAT

BOOLEAN

BOOLEAN

VARCHAR / VARCHAR(x)

STRING

VARBINARY

FIXED, BYTES

ARRAY

ARRAY

MAP

MAP

Avro schema evolution#

The Avro decoder supports schema evolution feature with backward compatibility. With backward compatibility, a newer schema can be used to read Avro data created with an older schema. Any change in the Avro schema must also be reflected in Trino’s topic definition file. Newly added/renamed fields must have a default value in the Avro schema file.

The schema evolution behavior is as follows:

  • Column added in new schema: Data created with an older schema produces a default value, when the table is using the new schema.

  • Column removed in new schema: Data created with an older schema no longer outputs the data from the column that was removed.

  • Column is renamed in the new schema: This is equivalent to removing the column and adding a new one, and data created with an older schema produces a default value when table is using the new schema.

  • Changing type of column in the new schema: If the type coercion is supported by Avro, then the conversion happens. An error is thrown for incompatible types.