​
Batch read settings

On a block-by-block basis, customize how it reads data from its upstream blocks.

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Read specific chunks

Downstream blocks can control the volume of data it loads into memory from its upstream block’s output. The downstream block will only load the chunk from indigo_mountain where power equals 5.
1

Load data

import random

import polars as pl

from mage_ai.data.tabular.mocks import create_dataframe


@data_loader
def load_data(*args, **kwargs):
    dfs = []
    for i in range(10):
        df = create_dataframe(n_rows=100_000, use_pandas=False)
        df = df.with_columns(pl.lit(i).alias('power'))
        if i == 5:
            df = df.with_columns(pl.lit(i).cast(pl.Float64).alias('col_0'))
        dfs.append(df)
    return pl.concat(dfs)
2

Transform data

@transformer
def transform(data, *args, **kwargs):
    return data

​
Input data types

Downstream blocks can control the strategy it implements when loading an upstream block’s output data into memory.

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Batch

Read data in batches. 
@transformer
def transform(data, *args, **kwargs):
    print('Batch size:', len(data[0]))
    print('Chunks:', len(data))

Batch size: 143
Chunks: 21105

​
Generator

Batch generator framework to operate and process 1,000+ gigabytes (GB) of data without running out of memory. 
@transformer
def transform(data, *args, **kwargs):
    for batch in data:
        df = batch.deserialize()
        print(df.shape)

(1337, 11)
(1337, 11)
(1337, 11)
(1337, 11)
(1337, 11)
(1337, 11)
(1337, 11)
(1337, 11)
(1337, 11)
(1337, 11)
...
1

Load data

import random

import polars as pl

from mage_ai.data.tabular.mocks import create_dataframe


@data_loader
def load_data(*args, **kwargs):
    dfs = []
    for i in range(10):
        df = create_dataframe(n_rows=100_000, use_pandas=False)
        df = df.with_columns(pl.lit(i).alias('power'))
        if i == 5:
            df = df.with_columns(pl.lit(i).cast(pl.Float64).alias('col_0'))
        dfs.append(df)
    return pl.concat(dfs)
2

Transform data

@transformer
def transform(data, *args, **kwargs):
    for batch in data:
        df = batch.deserialize()
        print(df.shape)

​
Reader

Invoke methods on the reader object directly. 
from mage_ai.data.tabular.reader import (
    read_metadata,
    sample_batch_datasets,
    scan_batch_datasets_generator,
)

@transformer
def transform(data, *args, **kwargs):
    print(data.chunks)
    print(data.number_of_outputs)
    print(data.resource_usages)
    print(data.variable_path)
    print(data.variable_type)
    print(data.data_source)

    output = data.read_sync()
    for batch in output:
        df = batch.deserialize()
        print(df.shape)

​
Batch write settings

Customize how data is written when outputting data from a block. 
import random

import polars as pl

from mage_ai.data.tabular.mocks import create_dataframe


@data_loader
def load_data(*args, **kwargs):
    dfs = []
    for i in range(10):
        df = create_dataframe(n_rows=100_000, use_pandas=False)
        df = df.with_columns(pl.lit(i).alias('power'))
        if i == 5:
            df = df.with_columns(pl.lit(i).cast(pl.Float64).alias('col_0'))
        dfs.append(df)
    return pl.concat(dfs)

​
Strategies

Explicitly set the chunking strategy when creating data partitions or batches of data. Example: create chunks by the value in column power

​
Items per chunk

Example: each chunk can only contain a maximum number of 7,777 items.

​
Number of chunks

Example: each chunk must have at least 1,337 items and the total number of chunks cannot exceed 143.

​
Byte size per check

Example: each chunk cannot exceed 100MB in size on disk.

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Modes

Control how data is written.
  1. Append
  2. Replace