, format, *, schema=None, mode='streaming', csv_settings=None, json_field_paths=None, object_pattern='*', with_metadata=False, persistent_id=None, autocommit_duration_ms=1500, debug_data=None, value_columns=None, primary_key=None, types=None, default_values=None, )

sourceReads a table from one or several files with the specified format.

In case the folder is passed to the engine, the order in which files from the directory are processed is determined according to the modification time of files within this folder: they will be processed by ascending order of the modification time.

In case the format is “plaintext”, the table will consist of a single column data with each cell containing a single line from the file.

  • Parameters
    • path (str | PathLike) – Path to the file or to the folder with files or glob pattern for the objects to be read. The connector will read the contents of all matching files as well as recursively read the contents of all matching folders.
    • format (str) – Format of data to be read. Currently “csv”, “json”, “plaintext”, “plaintext_by_file” and “binary” formats are supported. The difference between “plaintext” and “plaintext_by_file” is how the input is tokenized: if the “plaintext” option is chosen, it’s split by the newlines. Otherwise, the files are split in full and one row will correspond to one file. In case the “binary” format is specified, the data is read as raw bytes without UTF-8 parsing.
    • schema (type[Schema] | None) – Schema of the resulting table.
    • mode (str) – Denotes how the engine polls the new data from the source. Currently “streaming” and “static” are supported. If set to “streaming” the engine will wait for the updates in the specified directory. It will track file additions, deletions, and modifications and reflect these events in the state. For example, if a file was deleted,”streaming” mode will also remove rows obtained by reading this file from the table. On the other hand, the “static” mode will only consider the available data and ingest all of it in one commit. The default value is “streaming”.
    • csv_settings (CsvParserSettings | None) – Settings for the CSV parser. This parameter is used only in case the specified format is “csv”.
    • json_field_paths (dict[str, str] | None) – If the format is “json”, this field allows to map field names into path in the read json object. 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).
    • object_pattern (str) – Unix shell style pattern for filtering only certain files in the directory. Ignored in case a path to a single file is specified. This value will be deprecated soon, please use glob pattern in path instead.
    • 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 that will contain two optional fields - created_at and modified_at. These fields will have integral UNIX timestamps for the creation and modification time respectively. Additionally, the column will also have an optional field named owner that will contain the name of the file owner (applicable only for Un). Finally, the column will also contain a field named path that will show the full path to the file from where a row was filled.
    • 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.
    • debug_data (Any) – Static data replacing original one when debug mode is active.
    • value_columns (list[str] | None) – Names of the columns to be extracted from the files. [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 provided the default type is pw.Type.ANY. Supported in “csv” and “json” formats. [will be deprecated soon]
    • default_values (dict[str, Any] | None) – dictionary containing default values for columns replacing blank entriest value of the column must be specified explicitly, otherwise there will be no default value. [will be deprecated soon]
  • Returns
    Table – The table read.


Consider you want to read a dataset, stored in the filesystem in a standard CSV format. The dataset contains data about pets and their owners.

For the sake of demonstration, you can prepare a small dataset by creating a CSV file via a unix command line tool:

printf "id,owner,pet\n1,Alice,dog\n2,Bob,dog\n3,Alice,cat\n4,Bob,dog" > dataset.csv

In order to read it into Pathway’s table, you can first do the import and then use the method:

import pathway as pw
class InputSchema(pw.Schema):
  owner: str
  pet: str
t ="dataset.csv", format="csv", schema=InputSchema)

Then, you can output the table in order to check the correctness of the read:

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

Similarly, we can do the same for JSON format.

First, we prepare a dataset:

printf "{\"id\":1,\"owner\":\"Alice\",\"pet\":\"dog\"}
{\"id\":4,\"owner\":\"Bob\",\"pet\":\"cat\"}" > dataset.jsonlines

And then, we use the method with the “json” format:

t ="dataset.jsonlines", format="json", schema=InputSchema)

Now let’s try something different. Consider you have site access logs stored in a separate folder in several files. For the sake of simplicity, a log entry contains an access ID, an IP address and the login of the user.

A dataset, corresponding to the format described above can be generated, thanks to the following set of unix commands:

mkdir logs
printf "id,ip,login\n1,,alice\n2,,alice" > logs/part_1.csv
printf "id,ip,login\n3,,bob\n4,,alice" > logs/part_2.csv

Now, let’s see how you can use the connector in order to read the content of this directory into a table:

class InputSchema(pw.Schema):
  ip: str
  login: str
t ="logs/", format="csv", schema=InputSchema)

The only difference is that you specified the name of the directory instead of the file name, as opposed to what you had done in the previous example. It’s that simple!

Alternatively, we can do the same for the “json” variant:

The dataset creation would look as follows:

mkdir logs
printf "{\"id\":1,\"ip\":\"\",\"login\":\"alice\"}
{\"id\":2,\"ip\":\"\",\"login\":\"alice\"}" > logs/part_1.jsonlines
printf "{\"id\":3,\"ip\":\"\",\"login\":\"bob\"}
{\"id\":4,\"ip\":\"\",\"login\":\"alice\"}" > logs/part_2.jsonlines

While reading the data from logs folder can be expressed as:

t ="logs/", format="json", schema=InputSchema, mode="static")

But what if you are working with a real-time system, which generates logs all the time. The logs are being written and after a while they get into the log directory (this is also called “logs rotation”). Now, consider that there is a need to fetch the new files from this logs directory all the time. Would Pathway handle that? Sure!

The only difference would be in the usage of mode field. So the code snippet will look as follows:

t ="logs/", format="csv", schema=InputSchema, mode="streaming")

Or, for the “json” format case:

t ="logs/", format="json", schema=InputSchema, mode="streaming")

With this method, you obtain a table updated dynamically. The changes in the logs would incur changes in the Business-Intelligence ‘BI’-ready data, namely, in the tables you would like to output. Finally, a simple example for the plaintext format would look as follows:

t ="raw_dataset/lines.txt", format="plaintext"), filename, format)

sourceWrites table’s stream of updates to a file in the given format.

  • Parameters
    • table (Table) – Table to be written.
    • filename (str | PathLike) – Path to the target output file.
    • format (str) – Format to use for data output. Currently, there are two supported formats: “json” and “csv”.
  • Returns


In this simple example you can see how table output works. First, import Pathway and create a table:

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")

Consider you would want to output the stream of changes of this table in csv format. In order to do that you simply do:, "table.csv", format="csv")

Now, let’s see what you have on the output:

cat table.csv

The first three columns clearly represent the data columns you have. The column time represents the number of operations minibatch, in which each of the rows was read. In this example, since the data is static: you have 0. The diff is another element of this stream of updates. In this context, it is 1 because all three rows were read from the input. All in all, the extra information in time and diff columns - in this case - shows us that in the initial minibatch (time = 0), you have read three rows and all of them were added to the collection (diff = 1).

Alternatively, this data can be written in JSON format:, "table.jsonlines", format="json")

Then, we can also check the output file by executing the command:

cat table.jsonlines

As one can easily see, the values remain the same, while the format has changed to a plain JSON.