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pw.io.kafka

pw.io.kafka.read(rdkafka_settings, topic=None, *, schema=None, format='raw', debug_data=None, autocommit_duration_ms=1500, json_field_paths=None, autogenerate_key=False, with_metadata=False, start_from_timestamp_ms=None, parallel_readers=None, persistent_id=None, value_columns=None, primary_key=None, types=None, default_values=None, **kwargs)

sourceGeneralized method to read the data from the given topic in Kafka.

There are three formats currently supported: “plaintext”, “raw”, and “json”. If the “raw” format is chosen, the key and the payload are read from the topic as raw bytes and used in the table “as is”. If you choose the “plaintext” option, however, they are parsed from the UTF-8 into the plaintext entries. In both cases, the table consists of a primary key and a single column “data”, denoting the payload read.

If “json” is chosen, the connector first parses the payload of the message according to the JSON format and then creates the columns corresponding to the schema defined by the schema parameter. The values of these columns are taken from the respective parsed JSON fields.

  • Parameters
    • rdkafka_settings (dict) – Connection settings in the format of librdkafka.
    • topic (str | list[str] | None) – Name of topic in Kafka from which the data should be read.
    • schema (type[Schema] | None) – Schema of the resulting table.
    • format (str) – format of the input data, “raw”, “plaintext”, or “json”.
    • debug_data – Static data replacing original one when debug mode is active.
    • autocommit_duration_ms (int | None) – the maximum time between two commits. Every autocommit_duration_ms milliseconds, the updates received by the connector are committed and pushed into Pathway’s computation graph.
    • json_field_paths (dict[str, str] | None) – If the format is JSON, this field allows to map field names into path in the field. For the field which require such mapping, it should be given in the format <field_name>: <path to be mapped>, where the path to be mapped needs to be a JSON Pointer (RFC 6901).
    • autogenerate_key (bool) – If True, Pathway automatically generates unique primary key for the entries read. Otherwise it first tries to use the key from the message. This parameter is used only if the format is “raw” or “plaintext”.
    • with_metadata (bool) – When set to True, the connector will add an additional column named _metadata to the table. This column will be a JSON field. It’ll contain an optional field timestamp_millis denoting the UNIX timestamp of a record in milliseconds, if available. It will also contain fields topic, partition and offset denoting the topic, partition and offset respectively, that correspond to the Kafka message that produced this row.
    • start_from_timestamp_ms (int | None) – If defined, the read starts from entries with the given timestamp in the past, specified in milliseconds.
    • parallel_readers (int | None) – number of copies of the reader to work in parallel. In case the number is not specified, min{pathway_threads, total number of partitions} will be taken. This number also can’t be greater than the number of Pathway engine threads, and will be reduced to the number of engine threads, if it exceeds.
    • persistent_id (str | None) – (unstable) An identifier, under which the state of the table will be persisted or None, if there is no need to persist the state of this table. When a program restarts, it restores the state for all input tables according to what was saved for their persistent_id. This way it’s possible to configure the start of computations from the moment they were terminated last time.
    • value_columns (list[str] | None) – Columns to extract for a table, required for format other than “raw”. [will be deprecated soon]
    • primary_key (list[str] | None) – In case the table should have a primary key generated according to a subset of its columns, the set of columns should be specified in this field. Otherwise, the primary key will be generated randomly. [will be deprecated soon]
    • types (dict[str, PathwayType] | None) – Dictionary containing the mapping between the columns and the data types (pw.Type) of the values of those columns. This parameter is optional, and if not Otherwise, the primary key will be generated randomly. provided the default type is pw.Type.ANY. [will be deprecated soon]
    • default_values (dict[str, Any] | None) – dictionary containing default values for columns replacing blank entries. The default value of the column must be specified explicitly, Otherwise, the primary key will be generated randomly. otherwise there will be no default value. [will be deprecated soon]
  • Returns
    Table – The table read.

When using the format “raw”, the connector will produce a single-column table: all the data is saved into a column named data. For other formats, the argument value_column is required and defines the columns.

Example:

Consider there is a queue in Kafka, running locally on port 9092. Our queue can use SASL-SSL authentication over a SCRAM-SHA-256 mechanism. You can set up a queue with similar parameters in Upstash. Settings for rdkafka will look as follows:

import os
rdkafka_settings = {
   "bootstrap.servers": "localhost:9092",
   "security.protocol": "sasl_ssl",
   "sasl.mechanism": "SCRAM-SHA-256",
   "group.id": "$GROUP_NAME",
   "session.timeout.ms": "60000",
   "sasl.username": os.environ["KAFKA_USERNAME"],
   "sasl.password": os.environ["KAFKA_PASSWORD"]
}

To connect to the topic “animals” and accept messages, the connector must be used as follows, depending on the format:

Raw version:

import pathway as pw
t = pw.io.kafka.read(
  rdkafka_settings,
   topic="animals",
   format="raw",
)

All the data will be accessible in the column data.

CSV version:

import pathway as pw
class InputSchema(pw.Schema):
  owner: str
  pet: str
t = pw.io.kafka.read(
   rdkafka_settings,
   topic="animals",
   format="csv",
   schema=InputSchema
)

In case of CSV format, the first message must be the header:

owner,pet

Then, simple data rows are expected. For example:

Alice,cat
Bob,dog

This way, you get a table which looks as follows:

pw.debug.compute_and_print(t, include_id=False)

JSON version:

import pathway as pw
t = pw.io.kafka.read(
    rdkafka_settings,
    topic="animals",
    format="json",
    schema=InputSchema,
)

For the JSON connector, you can send these two messages:

{"owner": "Alice", "pet": "cat"}
{"owner": "Bob", "pet": "dog"}

This way, you get a table which looks as follows:

pw.debug.compute_and_print(t, include_id=False)

Now consider that the data about pets come in a more sophisticated way. For instance you have an owner, kind and name of an animal, along with some physical measurements.

The JSON payload in this case may look as follows:

{
    "name": "Jack",
    "pet": {
        "animal": "cat",
        "name": "Bob",
        "measurements": [100, 200, 300]
    }
}

Suppose you need to extract a name of the pet and the height, which is the 2nd (1-based) or the 1st (0-based) element in the array of measurements. Then, you use JSON Pointer and do a connector, which gets the data as follows:

import pathway as pw
class InputSchema(pw.Schema):
  pet_name: str
  pet_height: int
t = pw.io.kafka.read(
   rdkafka_settings,
   topic="animals",
   format="json",
   schema=InputSchema,
   json_field_paths={
       "pet_name": "/pet/name",
       "pet_height": "/pet/measurements/1"
   },
)

pw.io.kafka.read_from_upstash(endpoint, username, password, topic, *, read_only_new=False, schema=None, format='raw', debug_data=None, autocommit_duration_ms=1500, json_field_paths=None, parallel_readers=None, persistent_id=None)

sourceSimplified method to read data from Kafka instance hosted in Upstash. It requires endpoint address and topic along with credentials.

Read starts from the beginning of the topic, unless the read_only_new parameter is set to True.

There are three formats currently supported: “plaintext”, “raw”, and “json”. If the “raw” format is chosen, the key and the payload are read from the topic as raw bytes and used in the table “as is”. If you choose the “plaintext” option, however, they are parsed from the UTF-8 into the plaintext entries. In both cases, the table consists of a primary key and a single column “data”, denoting the payload read.

If “json” is chosen, the connector first parses the payload of the message according to the JSON format and then creates the columns corresponding to the schema defined by the schema parameter. The values of these columns are taken from the respective parsed JSON fields.

  • Parameters
    • endpoint (str) – Upstash endpoint for the sought queue, which can be found on “Details” page.
    • username (str) – Username generated for this queue.
    • password (str) – Password generated for this queue. These credentials are also available on “Details” page.
    • topic (str) – Name of topic in Kafka from which the data should be read.
    • read_only_new (bool) – If set to True only the entries which appear after the start of the program will be read. Otherwise, the read will be done from the beginning of thetopic.
    • schema (type[Schema] | None) – Schema of the resulting table.
    • format (str) – format of the input data, “raw”, “plaintext”, or “json”.
    • debug_data – Static data replacing original one when debug mode is active.
    • autocommit_duration_ms (int | None) – The maximum time between two commits. Every autocommit_duration_ms milliseconds, the updates received by the connector are committed and pushed into Pathway’s computation graph.
    • json_field_paths (dict[str, str] | None) – If the format is JSON, this field allows to map field names into path in the field. For the fields which require such mapping, it should be given in the format <field_name>: <path to be mapped>, where the path to be mapped needs to be a JSON Pointer (RFC 6901).
    • parallel_readers (int | None) – number of copies of the reader to work in parallel. In case the number is not specified, min{pathway_threads, total number of partitions} will be taken. This number also can’t be greater than the number of Pathway engine threads, and will be reduced to the number of engine threads, if it exceeds.
    • persistent_id (str | None) – (unstable) An identifier, under which the state of the table will be persisted or None, if there is no need to persist the state of this table. When a program restarts, it restores the state for all input tables according to what was saved for their persistent_id. This way it’s possible to configure the start of computations from the moment they were terminated last time.
  • Returns
    Table – The table read.

When using the format “raw”, the connector will produce a single-column table: all the data is saved into a column named data.

Example:

Consider that there is a queue running in Upstash. Let’s say the endpoint name is “https://example-endpoint.com:19092”, topic is “test-topic” and the credentials are stored in environment variables.

Suppose that we need just to read the raw messages for the further processing. Then it can be done in the following way:

import os
import pathway as pw
t = pw.io.kafka.read_from_upstash(
    endpoint="https://example-endpoint.com:19092",
    topic="test-topic",
    username=os.environ["KAFKA_USERNAME"],
    password=os.environ["KAFKA_PASSWORD"],
)

pw.io.kafka.simple_read(server, topic, *, read_only_new=False, schema=None, format='raw', debug_data=None, autocommit_duration_ms=1500, json_field_paths=None, parallel_readers=None, persistent_id=None)

sourceSimplified method to read data from Kafka. Only requires the server address and the topic name. If you have any kind of authentication or require fine-tuning of the parameters, please use read method.

Read starts from the beginning of the topic, unless the read_only_new parameter is set to True.

There are three formats currently supported: “plaintext”, “raw”, and “json”. If the “raw” format is chosen, the key and the payload are read from the topic as raw bytes and used in the table “as is”. If you choose the “plaintext” option, however, they are parsed from the UTF-8 into the plaintext entries. In both cases, the table consists of a primary key and a single column “data”, denoting the payload read.

If “json” is chosen, the connector first parses the payload of the message according to the JSON format and then creates the columns corresponding to the schema defined by the schema parameter. The values of these columns are taken from the respective parsed JSON fields.

  • Parameters
    • server (str) – Address of the server.
    • topic (str) – Name of topic in Kafka from which the data should be read.
    • read_only_new (bool) – If set to True only the entries which appear after the start of the program will be read. Otherwise, the read will be done from the beginning of thetopic.
    • schema (type[Schema] | None) – Schema of the resulting table.
    • format (str) – format of the input data, “raw”, “plaintext”, or “json”.
    • debug_data – Static data replacing original one when debug mode is active.
    • autocommit_duration_ms (int | None) – The maximum time between two commits. Every autocommit_duration_ms milliseconds, the updates received by the connector are committed and pushed into Pathway’s computation graph.
    • json_field_paths (dict[str, str] | None) – If the format is JSON, this field allows to map field names into path in the field. For the fields which require such mapping, it should be given in the format <field_name>: <path to be mapped>, where the path to be mapped needs to be a JSON Pointer (RFC 6901).
    • parallel_readers (int | None) – number of copies of the reader to work in parallel. In case the number is not specified, min{pathway_threads, total number of partitions} will be taken. This number also can’t be greater than the number of Pathway engine threads, and will be reduced to the number of engine threads, if it exceeds.
    • persistent_id (str | None) – (unstable) An identifier, under which the state of the table will be persisted or None, if there is no need to persist the state of this table. When a program restarts, it restores the state for all input tables according to what was saved for their persistent_id. This way it’s possible to configure the start of computations from the moment they were terminated last time.
  • Returns
    Table – The table read.

When using the format “raw”, the connector will produce a single-column table: all the data is saved into a column named data.

For other formats, the argument value_column is required and defines the columns.

Example:

Consider that there’s a Kafka queue running locally on the port 9092 and we need to read raw messages from the topic “test-topic”. Then, it can be done in the following way:

import pathway as pw
t = pw.io.kafka.simple_read("localhost:9092", "test-topic")

pw.io.kafka.write(table, rdkafka_settings, topic_name, *, format='json', delimiter=',', key=None, value=None, headers=None)

sourceWrite a table to a given topic on a Kafka instance.

The produced messages consist of the key, corresponding to row’s key, the value, corresponding to the values of the table that are serialized according to the chosen format and two headers: pathway_time, corresponding to the logical time of the entry and pathway_diff that is either 1 or -1. Both header values are provided as UTF-8 encoded strings.

There are several serialization formats supported: ‘json’, ‘dsv’, ‘plaintext’ and ‘raw’. The format defines how the message is formed. In case of JSON and DSV (delimiter separated values), the message is formed in accordance with the respective data format.

If the selected format is either ‘plaintext’ or ‘raw’, you also need to specify, which columns of the table correspond to the key and the value of the produced Kafka message. It can be done by providing key and value parameters. In order to output extra values from the table in these formats, Kafka headers can be used. You can specify the column references in the headers parameter, which leads to serializing the extracted fields into UTF-8 strings and passing them as additional Kafka headers.

  • Parameters
    • table (Table) – the table to output.
    • rdkafka_settings (dict) – Connection settings in the format of librdkafka.
    • topic_name (str) – name of topic in Kafka to which the data should be sent.
    • format (str) – format in which the data is put into Kafka. Currently “json”, “plaintext”, “raw” and “dsv” are supported. If the “raw” format is selected, table must either contain exactly one binary column that will be dumped as it is into the Kafka message, or the reference to the target binary column must be specified explicitly in the value parameter. Similarly, if “plaintext” is chosen, the table should consist of a single column of the string type.
    • delimiter (str) – field delimiter to be used in case of delimiter-separated values format.
    • key (ColumnReference | None) – reference to the column that should be used as a key in the produced message in ‘plaintext’ or ‘raw’ format. If left empty, an internal primary key will be used.
    • value (ColumnReference | None) – reference to the column that should be used as a value in the produced message in ‘plaintext’ or ‘raw’ format. It can be deduced automatically if the table has exactly one column. Otherwise it must be specified directly. It also has to be explicitly specified, if key is set.
    • headers (Optional[Iterable[ColumnReference]]) – references to the table fields that must be provided as message headers. These headers are named in the same way as fields that are forwarded and correspond to the string representations of the respective values encoded in UTF-8. If a binary column is requested, it will be produced “as is” in the respective header.
  • Returns
    None

Examples:

Consider there is a queue in Kafka, running locally on port 9092. Our queue can use SASL-SSL authentication over a SCRAM-SHA-256 mechanism. You can set up a queue with similar parameters in Upstash. Settings for rdkafka will look as follows:

import os
rdkafka_settings = {
   "bootstrap.servers": "localhost:9092",
   "security.protocol": "sasl_ssl",
   "sasl.mechanism": "SCRAM-SHA-256",
   "sasl.username": os.environ["KAFKA_USERNAME"],
   "sasl.password": os.environ["KAFKA_PASSWORD"]
}

You want to send a Pathway table t to the Kafka instance.

import pathway as pw
t = pw.debug.table_from_markdown("age owner pet \n 1 10 Alice dog \n 2 9 Bob cat \n 3 8 Alice cat")

To connect to the topic “animals” and send messages, the connector must be used as follows, depending on the format:

JSON version:

pw.io.kafka.write(
   t,
   rdkafka_settings,
   "animals",
   format="json",
)

All the updates of table t will be sent to the Kafka instance.

Another thing to be demonstated is the usage of ‘raw’ format in the output. Please note that the same rules will be applicable for the ‘plaintext’ with the only difference being the requirement for the columns to have the string type.

Now consider that a table t2 contains two binary columns foo and bar, and a numerical column baz. That is, the schema of this table looks as follows:

class T2Schema(pw.Schema):
    foo: bytes
    bar: bytes
    baz: int

This table can be generated with a Python input connector as follows:

class T2GenerationSubject(pw.python.ConnectorSubject):
    def run(self) -> None:
        # TODO: define generation logic
        pass
t2 = pw.io.python.read(T2GenerationSubject(), schema=T2Schema)

Since is more than one column, you need to specify which one you want to use in the output, when using the ‘raw’ format. If this is the column foo, you may output this table as follows:

pw.io.kafka.write(
   t2,
   rdkafka_settings,
   "test",
   format="raw",
   value=t2.foo,
)

If at the same time you would prefer to have the key of the produced messages to be defined by the value of another binary column bar, you can use the key parameter as follows:

pw.io.kafka.write(
   t2,
   rdkafka_settings,
   "test",
   format="raw",
   key=t2.bar,
   value=t2.foo,
)

Still, the table has three fields and the field baz is not produced. You can do it with the usage of headers. To pass it to the header with the same name baz, you need to specify it:

pw.io.kafka.write(
   t2,
   rdkafka_settings,
   "test",
   format="raw",
   key=t2.bar,
   value=t2.foo,
   headers=[t2.baz],
)