62k

pw.io.nats

read(uri, topic, *, schema=None, format='raw', autocommit_duration_ms=1500, json_field_paths=None, jetstream_stream_name=None, durable_consumer_name=None, parallel_readers=None, name=None, max_backlog_size=None, debug_data=None, **kwargs)

sourceReads data from a specified NATS topic.

It supports three formats: "plaintext", "raw", and "json".

For the "raw" format, the payload is read as raw bytes and added directly to the table. In the "plaintext" format, the payload decoded from UTF-8 and stored as plain text. In both cases, the table will have an autogenerated primary key and a single "data" column representing the payload.

If you select the "json" format, the connector parses the message payload as JSON and creates table columns based on the schema provided in the schema parameter. The column values come from the corresponding JSON fields.

The JetStream extension is supported. To read a NATS topic from a JetStream stream, specify the jetstream_stream_name parameter. When this parameter is provided, Pathway will use the given stream to read data.

You can also use your own durable JetStream pull consumer if you already have one. To do this, specify the durable_consumer_name parameter. Otherwise, Pathway will automatically create a durable consumer for you with an auto-generated name. This name will remain the same as long as you do not change your set of input sources.

  • Parameters
    • uri (str) – The URI of the NATS server.
    • topic (str) – The name of the NATS topic to read data from.
    • schema (type[Schema] | None) – The table schema, used only when the format is set to "json".
    • format (Literal['plaintext', 'raw', 'json']) – The input data format, which can be "raw", "plaintext", or "json".
    • autocommit_duration_ms (int | None) – The time interval (in milliseconds) between commits. After this time, the updates received by the connector are committed and added to Pathway’s computation graph.
    • json_field_paths (dict[str, str] | None) – For the "json" format, this allows mapping field names to paths within the JSON structure. Use the format <field_name>: <path> where the path follows the JSON Pointer (RFC 6901).
    • jetstream_stream_name (str | None) – if specified, the JetStream https://docs.nats.io/nats-concepts/jetstream_ extension is used. In this case, the specified stream will be used.
    • durable_consumer_name (str | None) – the name of the durable pull consumer to use with JetStream. If not specified, the consumer will be created automatically with default settings. The consumer’s name will remain the same unless the set of input sources is changed.
    • parallel_readers (int | None) – The number of reader instances running in parallel. If not specified, it defaults to min(pathway_threads, total_partitions). It can’t exceed the number of Pathway engine threads and will be reduced if necessary.
    • name (str | None) – A unique name for the connector. If provided, this name will be used in logs and monitoring dashboards. Additionally, if persistence is enabled, it will be used as the name for the snapshot that stores the connector’s progress.
    • max_backlog_size (int | None) – Limit on the number of entries read from the input source and kept in processing at any moment. Reading pauses when the limit is reached and resumes as processing of some entries completes. Useful with large sources that emit an initial burst of data to avoid memory spikes.
    • debug_data – Static data replacing original one when debug mode is active.
  • Returns
    Table – The table read.

Example:

To run local tests, you can download the nats-server binary from the Releases page and start it. By default, it runs on port 4222 at localhost.

If your NATS server is running on localhost using the default port, you can stream the "data" topic to a Pathway table like this:

import pathway as pw
table = pw.io.nats.read("nats://127.0.0.1:4222", "data")

Keep in mind that NATS doesn’t normally store messages. So, make sure to start your Pathway program before sending any messages.

You can also parse messages as UTF-8 during reading by using the "format" parameter. Here’s how the reading process would look:

table = pw.io.nats.read("nats://127.0.0.1:4222", "data", format="plaintext")

Alternatively, you can read and parse a JSON table during the reading process by using the "json" format and the schema parameter.

For example, if your data is in JSON format with three fields - an integer user_id (which you’d like to use as the primary key instead of an autogenerated one), and two string fields username and phone - you can define the schema like this:

class InputSchema(pw.Schema):
    user_id: int = pw.column_definition(primary_key=True)
    username: str
    phone: str

Now, you can use the format and schema parameters of the connector like this:

table = pw.io.nats.read(
    "nats://127.0.0.1:4222",
    "data",
    format="json",
    schema=InputSchema,
)

As a result, you will have a table with three columns: "user_id", "username", and "phone". The "user_id" column will also act as the primary key for the Pathway table.

If you are using the JetStream extension for your NATS connector, you need to provide the jetstream_stream_name field, specifying name of the persistent data stream. The code then looks as follows:

table = pw.io.nats.read(
    "nats://127.0.0.1:4222",
    "data",
    format="json",
    schema=InputSchema,
    jetstream_stream_name="your_stream_name",
)

Note that the autogenerated name of the durable consumer won’t change. Therefore, if you restart your Pathway program, this configuration will only read the new messages that were added after the last execution. For example, if two new messages arrived since the previous run, only those two messages will be read.

If desired, you can also specify the name of your own durable consumer by setting the durable_consumer_name field. This allows Pathway to use your existing durable consumer instead of creating a new one automatically.

table = pw.io.nats.read(
    "nats://127.0.0.1:4222",
    "data",
    format="json",
    schema=InputSchema,
    jetstream_stream_name="your_stream_name",
    durable_consumer_name="your_consumer_name",
)

write(table, uri, topic, *, format='json', delimiter=',', jetstream_stream_name=None, value=None, headers=None, name=None, sort_by=None)

sourceWrites data into the specified NATS topic.

The produced messages consist of the payload, corresponding to the values of the table that are serialized according to the chosen format and two headers: pathway_time, corresponding to the processing time of the entry and pathway_diff that is either 1 or -1. Both header values are provided as UTF-8 encoded strings. If headers parameter is used, additional headers can be added to the message.

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 column of the table correspond to the payload of the produced NATS message. It can be done by providing value parameter. In order to output extra values from the table in these formats, NATS 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 message headers.

When using the JetStream extension, you need to specify the name of the stream that is used for data persistence. Provide this name in the jetstream_stream_name field to ensure that Pathway writes to the correct persistent stream.

  • Parameters
    • table (Table) – The table for output.
    • uri (str) – The URI of the NATS server.
    • topic (str | ColumnReference) – The NATS topic where data will be written. This can be a specific topic name or a reference to a column whose values will be used as the topic for each message. If using a column reference, the column must contain string values.
    • format (Literal['json', 'dsv', 'plaintext', 'raw']) – format in which the data is put into NATS. 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 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.
    • jetstream_stream_name (str | None) – if specified, the JetStream https://docs.nats.io/nats-concepts/jetstream_ extension is used. In this case, the specified stream will be used.
    • value (ColumnReference | None) – reference to the column that should be used as a payload 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.
    • 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. Note that due to NATS constraints imposed on headers, the binary fields must also be UTF-8 serializable.
    • name (str | None) – A unique name for the connector. If provided, this name will be used in logs and monitoring dashboards.
    • sort_by (Optional[Iterable[ColumnReference]]) – If specified, the output will be sorted in ascending order based on the values of the given columns within each minibatch. When multiple columns are provided, the corresponding value tuples will be compared lexicographically.

Example:

Assume you have the NATS server running locally on the default port, 4222. Let’s explore a few ways to send the contents of a table to the topic test_topic on this server.

First, you’ll need to create a Pathway table. You can do this using the table_from_markdown method to set up a test table with information about pets and their owners.

import pathway as pw

table = pw.debug.table_from_markdown('''
age | owner | pet
10  | Alice | dog
9   | Bob   | cat
8   | Alice | cat
''')

To output the table’s contents in JSON format, use the connector like this:

pw.io.nats.write(
    table,
    "nats://127.0.0.1:4222",
    "test_topic",
    format="json",
)

In this case, the output will include the table’s rows in JSON format, with time and diff fields added to each JSON payload.

You can also use a single column from the table as the payload. For instance, to use the owner column as the NATS message payload, implement it as follows:

pw.io.nats.write(
    table,
    "nats://127.0.0.1:4222",
    "test_topic",
    format="plaintext",
    value=table.owner,
)

If needed, you can also send the remaining fields as headers. To do this, modify the code to use the headers field, which should include all the required fields. Since owner is already being sent as the message payload, you can add the age and pet columns to the headers. Here’s what the code would look like:

pw.io.nats.write(
    table,
    "nats://127.0.0.1:4222",
    "test_topic",
    format="plaintext",
    value=table.owner,
    headers=[table.age, table.pet],
)

If you are using JetStream, you need to specify an additional parameter jetstream_stream_name, where you indicate the name of the existing stream in the JetStream.

pw.io.nats.write(
    table,
    "nats://127.0.0.1:4222",
    "test_topic",
    jetstream_stream_name="your_stream_name",
    format="plaintext",
    value=table.owner,
    headers=[table.age, table.pet],
)