Loader file format

Configure

To set the pipeline's loader file format, you can either:

1. Set the parameter at the resource level: @dlt.resource(file_format=...) (learn more)

2. Set the parameter at the pipeline level: pipeline.run(..., loader_file_format=...)

Parquet

Apache Parquet is a free and open-source column-oriented data storage format in the Apache Hadoop ecosystem. dlt is capable of storing data in this format when configured to do so.

To use this format, you need the pyarrow package. You can get this package as a dlt extra as well:

pip install "dlt[parquet]"

Destination autoconfig

dlt uses destination capabilities to configure the parquet writer:

• It uses decimal and wei precision to pick the right decimal type and sets precision and scale.
• It uses timestamp precision to pick the right timestamp type resolution (seconds, microseconds, or nanoseconds).
• It uses supports_dictionary_encoding to control whether constant columns (like _dlt_load_id) use dictionary-encoded Arrow arrays. Dictionary encoding is memory-efficient for repeated values but not supported by all destinations. Defaults to true.

Writer settings

Under the hood, dlt uses the pyarrow parquet writer to create the files. The following options can be used to change the behavior of the writer:

• flavor: Sanitize schema or set other compatibility options to work with various target systems. Defaults to None, which is the pyarrow default.
• version: Determine which Parquet logical types are available for use, whether the reduced set from the Parquet 1.x.x format or the expanded logical types added in later format versions. Defaults to "2.6".
• data_page_size: Set a target threshold for the approximate encoded size of data pages within a column chunk (in bytes). Defaults to None, which is the pyarrow default.
• row_group_size: Set the number of rows in a row group. See here how this can optimize parallel processing of queries on your destination over the default setting of pyarrow.
• timestamp_timezone: A string specifying the timezone, default is UTC.
• coerce_timestamps: resolution to which to coerce timestamps, choose from s, ms, us, ns
• allow_truncated_timestamps - will raise if precision is lost on truncated timestamps.
• write_page_index: Boolean specifying whether a page index is written. Defaults to False.
• use_content_defined_chunking: Boolean specifying whether Content-Defined Chunking is used. Defaults to False. Requires pyarrow>=21.0.0, ignored otherwise.
• arrow_concat_promote_options: Controls type promotion when concatenating multiple Arrow tables/DataFrames. Accepts "none" (default), "default", or "permissive". See Handling schema mismatches for details.

tip

The default parquet version used by dlt is 2.4. It coerces timestamps to microseconds and truncates nanoseconds silently. Such a setting provides the best interoperability with database systems, including loading pandas DataFrames which have nanosecond resolution by default.

Read the pyarrow parquet docs to learn more about these settings.

Example:

[data_writer]
# the default values
flavor="spark"
version="2.4"
data_page_size=1048576
timestamp_timezone="Europe/Berlin"

Or using environment variables:

DATA_WRITER__FLAVOR
DATA_WRITER__VERSION
DATA_WRITER__DATA_PAGE_SIZE
DATA_WRITER__TIMESTAMP_TIMEZONE
DATA_WRITER__ARROW_CONCAT_PROMOTE_OPTIONS

tip

You can apply data writer settings to parquet created in normalize stage only: NORMALIZE__DATA_WRITER__FLAVOR=spark

or when your source/resource yields arrow tables / pandas DataFrames, you can control settings per source SOURCES__<SOURCE_MODULE>__<SOURCE_NAME>__DATA_WRITER__FLAVOR=spark

Find more similar examples here

Timestamps and timezones

dlt adds timezone (UTC adjustment) to all timestamps regardless of the precision (from seconds to nanoseconds). dlt will also create TZ-aware timestamp columns in the destinations. DuckDB is an exception here.

Disable timezones / UTC adjustment flags

You can generate parquet files without timezone adjustment information in two ways:

1. Set the flavor to spark. All timestamps will be generated via the deprecated int96 physical data type, without the logical one.
2. Set the timestamp_timezone to an empty string (i.e., DATA_WRITER__TIMESTAMP_TIMEZONE="") to generate a logical type without UTC adjustment.

To our best knowledge, Arrow will convert your timezone-aware DateTime(s) to UTC and store them in parquet without timezone information.

Row group size

The pyarrow parquet writer writes each item, i.e., table or record batch, in a separate row group. This may lead to many small row groups, which may not be optimal for certain query engines. For example, duckdb parallelizes on a row group. dlt allows controlling the size of the row group by buffering and concatenating tables and batches before they are written. The concatenation is done as a zero-copy to save memory. You can control the size of the row group by setting the maximum number of rows kept in the buffer.

[data_writer]
buffer_max_items=10e6

Keep in mind that dlt holds the tables in memory. Thus, 10,000,000 rows in the example above may consume a significant amount of RAM.

The row_group_size configuration setting has limited utility with the pyarrow writer. It may be useful when you write single very large pyarrow tables or when your in-memory buffer is really large.

CSV

CSV is the most basic file format for storing tabular data, where all values are strings and are separated by a delimiter (typically a comma). dlt uses it for specific use cases - mostly for performance and compatibility reasons.

Internally, we use two implementations:

• Python standard library CSV writer
• PyArrow CSV writer - a very fast, multithreaded writer for Arrow tables

Settings

dlt attempts to make both writers generate similarly looking files:

• separators are commas
• quotes are " and are escaped as ""
• NULL values are both empty strings and empty tokens as in the example below
• UNIX new lines ("\n") are used by default
• dates are represented as ISO 8601
• quoting style is "when needed"

Example of NULLs:

text1,text2,text3
A,B,C
A,,""

In the last row, both text2 and text3 values are NULL. The Python csv writer is not able to write unquoted None values, so we had to settle for "".

Note: all destinations capable of writing CSVs must support it.

You can change basic csv settings; this may be handy when working with the filesystem destination. Other destinations are tested with standard settings:

• delimiter: change the delimiting character (default: ',')

• include_header: include the header row (default: True)

• lineterminator: specify the string used to terminate lines (default: \n - UNIX line endings, use \r\n for Windows line endings)

• quoting: controls when quotes should be generated around field values. Available options:

  • quote_needed (default): quote only values that need quoting, i.e., non-numeric values
    • Python CSV writer: All non-numeric values are quoted
    • PyArrow CSV writer: The exact behavior is not fully documented. We observed that in some cases, strings are not quoted as well
  • quote_all: all values are quoted
    • Supported by both Python CSV writer and PyArrow CSV writer
  • quote_minimal: quote only fields containing special characters (delimiter, quote character, or line terminator)
    • Supported by Python CSV writer only
  • quote_none: never quote fields
    • Python CSV writer: Uses escape character when delimiter appears in data
    • PyArrow CSV writer: Raises an error if data contains special characters

[normalize.data_writer]
delimiter="|"
include_header=false
quoting="quote_all"
lineterminator="\r\n"

Or using environment variables:

NORMALIZE__DATA_WRITER__DELIMITER=|
NORMALIZE__DATA_WRITER__INCLUDE_HEADER=False
NORMALIZE__DATA_WRITER__QUOTING=quote_all
NORMALIZE__DATA_WRITER__LINETERMINATOR=$"\r\n"

Note the "$" prefix before "\r\n" to escape the newline character when using environment variables.

A few additional settings are available when copying csv to destination tables:

• on_error_continue - skip lines with errors (only Snowflake)
• encoding - encoding of the csv file

tip

You'll need these settings when importing external files.

Limitations

arrow writer

• binary columns are supported only if they contain valid UTF-8 characters
• json (nested, struct) types are not supported

csv writer

• binary columns are supported only if they contain valid UTF-8 characters (easy to add more encodings)
• json columns dumped with json.dumps
• None values are always quoted

JSONL

JSONL (or JSON Lines, JSON Delimited) is a file format that stores several JSON documents in one file. The JSON documents are separated by a new line.

Additional data types are stored as follows:

• datetime and date are stored as ISO strings;
• decimal is stored as a text representation of a decimal number;
• binary is stored as a base64 encoded string;
• HexBytes is stored as a hex encoded string;
• json is serialized as a string.

This file format is compressed by default.

SQL INSERT

This file format contains an INSERT...VALUES statement to be executed on the destination during the load stage.

Additional data types are stored as follows:

• datetime and date are stored as ISO strings;
• decimal is stored as a text representation of a decimal number;
• binary storage depends on the format accepted by the destination;
• json storage also depends on the format accepted by the destination.

This file format is compressed by default.