pw.io.csv

pw.io.csv.read(path, value_columns=None, *, schema=None, csv_settings=None, mode='streaming', object_pattern='*', with_metadata=False, autocommit_duration_ms=1500, persistent_id=None, debug_data=None, id_columns=None, types=None, default_values=None, **kwargs)

sourceReads a table from one or several files with delimiter-separated values.

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.

• 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.
  • value_columns (list[str] | None) – Names of the columns to be extracted from the files. [will be deprecated soon]
  • schema (type[Schema] | None) – Schema of the resulting table.
  • id_columns (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]
  • csv_settings (CsvParserSettings | None) – Settings for the CSV parser.
  • 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”.
  • 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.
  • 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. [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 there will be no default value. [will be deprecated soon]
  • 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.
  • 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 – Static data replacing original one when debug mode is active.
• Returns
  Table – The table read.

Example:

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 pw.io.csv.read method:

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

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

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

owner pet
Alice dog
  Bob dog
Alice cat
  Bob dog

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\n1,127.0.0.1,alice\n2,8.8.8.8,alice" > logs/part_1.csv
printf "id,ip,login\n3,8.8.8.8,bob\n4,127.0.0.1,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 = pw.io.csv.read("logs/", schema=InputSchema, mode="static")

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!

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 flag. So the code snippet will look as follows:

t = pw.io.csv.read("logs/", 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. article.

pw.io.csv.write(table, filename)

sourceWrites table’s stream of updates to a file in delimiter-separated values format.

• Parameters
  • table (Table) – Table to be written.
  • filename (str | PathLike) – Path to the target output file.
• Returns
  None

Example:

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 order to do that you simply do:

pw.io.csv.write(t, "table.csv")

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

cat table.csv

age,owner,pet,time,diff
10,"Alice","dog",0,1
9,"Bob","cat",0,1
8,"Alice","cat",0,1

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).