Access loaded data in Python

This guide explains how to access and manipulate data that has been loaded into your destination using the dlt Python library. After running your pipelines and loading data, you can use the pipeline.dataset() and data frame expressions, Ibis or SQL to query the data and read it as records, Pandas frames or Arrow tables.

Quick start example

Here's a full example of how to retrieve data from a pipeline and load it into a Pandas DataFrame or a PyArrow Table.

# Assuming you have a Pipeline object named 'pipeline'. You can create one with the dlt cli: dlt init fruitshop duckdb
# and you have loaded the data of the fruitshop example source into the destination
# the tables available in the destination are:
# - customers
# - inventory
# - purchases

# Step 1: Get the readable dataset from the pipeline
dataset = pipeline.dataset()

# Step 2: Access a table as a ReadableRelation
customers_relation = dataset.customers  # Or dataset["customers"]

# Step 3: Fetch the entire table as a Pandas DataFrame
df = customers_relation.df()

# Alternatively, fetch as a PyArrow Table
arrow_table = customers_relation.arrow()

Getting started

Assuming you have a Pipeline object (let's call it pipeline), you can obtain a ReadableDataset and access your tables as ReadableRelation objects.

Note: The ReadableDataset and ReadableRelation objects are lazy-loading. They will only query and retrieve data when you perform an action that requires it, such as fetching data into a DataFrame or iterating over the data. This means that simply creating these objects does not load data into memory, making your code more efficient.

Access the dataset

# Get the readable dataset from the pipeline
dataset = pipeline.dataset()

# print the row counts of all tables in the destination as dataframe
print(dataset.row_counts().df())

Access tables as dataset

You can access tables in your dataset using either attribute access or item access.

# Using attribute access
customers_relation = dataset.customers

# Using item access
customers_relation = dataset["customers"]

Reading data

Once you have a ReadableRelation, you can read data in various formats and sizes.

Fetch the entire table

caution

Loading full tables into memory without limiting or iterating over them can consume a large amount of memory and may cause your program to crash if the table is too large. It's recommended to use chunked iteration or apply limits when dealing with large datasets.

As a Pandas DataFrame

df = customers_relation.df()

As a PyArrow Table

arrow_table = customers_relation.arrow()

As a list of Python tuples

items_list = customers_relation.fetchall()

Lazy loading behavior

The ReadableDataset and ReadableRelation objects are lazy-loading. This means that they do not immediately fetch data when you create them. Data is only retrieved when you perform an action that requires it, such as calling .df(), .arrow(), or iterating over the data. This approach optimizes performance and reduces unnecessary data loading.

Iterating over data in chunks

To handle large datasets efficiently, you can process data in smaller chunks.

Iterate as Pandas DataFrames

for df_chunk in customers_relation.iter_df(chunk_size=5):
    # Process each DataFrame chunk
    pass

Iterate as PyArrow Tables

for arrow_chunk in customers_relation.iter_arrow(chunk_size=5):
    # Process each PyArrow chunk
    pass

Iterate as lists of tuples

for items_chunk in customers_relation.iter_fetch(chunk_size=5):
    # Process each chunk of tuples
    pass

The methods available on the ReadableRelation correspond to the methods available on the cursor returned by the SQL client. Please refer to the SQL client guide for more information.

Connection Handling

For every call that actually fetches data from the destination, such as df(), arrow(), fetchall() etc., the dataset will open a connection and close it after it has been retrieved or the iterator is completed. You can keep the connection open for multiple requests with the dataset context manager:

# the dataset context manager will keep the connection open
# and close it after the with block is exited
with dataset as dataset_:
    print(dataset.customers.limit(50).arrow())
    print(dataset.purchases.arrow())

Special queries

You can use the row_counts method to get the row counts of all tables in the destination as a DataFrame.

# print the row counts of all tables in the destination as dataframe
print(dataset.row_counts().df())

# or as tuples
print(dataset.row_counts().fetchall())

Modifying queries

You can refine your data retrieval by limiting the number of records, selecting specific columns, or chaining these operations.

Limit the number of records

# Get the first 50 items as a PyArrow table
arrow_table = customers_relation.limit(50).arrow()

Using head() to get the first 5 records

df = customers_relation.head().df()

Select specific columns

# Select only 'id' and 'name' columns
items_list = customers_relation.select("id", "name").fetchall()

# Alternate notation with brackets
items_list = customers_relation[["id", "name"]].fetchall()

# Only get one column
items_list = customers_relation[["name"]].fetchall()

Chain operations

You can combine select, limit, and other methods.

# Select columns and limit the number of records
arrow_table = customers_relation.select("id", "name").limit(50).arrow()

Modifying queries with ibis expressions

If you install the amazing ibis library, you can use ibis expressions to modify your queries.

pip install ibis-framework

dlt will then wrap an ibis.UnboundTable with a ReadableIbisRelation object under the hood that will allow you to modify the query of a reltaion using ibis expressions:

# now that ibis is installed, we can get a dataset with ibis relations
dataset = pipeline.dataset()

# get two relations
customers_relation = dataset["customers"]
purchases_relation = dataset["purchases"]

# join them using an ibis expression
joined_relation = customers_relation.join(
    purchases_relation, customers_relation.id == purchases_relation.customer_id
)

# now we can use the ibis expression to filter the data
filtered_relation = joined_relation.filter(purchases_relation.quantity > 1)

# we can inspect the query that will be used to read the data
print(filtered_relation.query)

# and finally fetch the data as a pandas dataframe, the same way we would do with a normal relation
df = filtered_relation.df()

# a few more examples

# get all customers from berlin and london
customers_relation.filter(customers_relation.city.isin(["berlin", "london"])).df()

# limit and offset
customers_relation.limit(10, offset=5).arrow()

# mutate columns by adding a new colums that always is 10 times the value of the id column
customers_relation.mutate(new_id=customers_relation.id * 10).df()

# sort asc and desc
import ibis

customers_relation.order_by(ibis.desc("id"), ibis.asc("city")).limit(10)

# group by and aggregate
customers_relation.group_by("city").having(customers_relation.count() >= 3).aggregate(
    sum_id=customers_relation.id.sum()
).df()

# subqueries
customers_relation.filter(customers_relation.city.isin(["berlin", "london"])).df()

You can learn more about the available expressions on the ibis for sql users page.

note

Keep in mind that you can use only methods that modify the executed query and none of the methods ibis provides for fetching data. This is done with the same methods defined on the regular relations explained above. If you need full native ibis integration, please read the ibis section in the advanced part further down. Additionally, not all ibis expressions may be supported by all destinations and sql dialects.

Supported destinations

All SQL and filesystem destinations supported by dlt can utilize this data access interface.

Reading data from filesystem

For filesystem destinations, dlt uses DuckDB under the hood to create views on iceberg and delta tables or from Parquet, JSONL and csv files. This allows you to query data stored in files using the same interface as you would with SQL databases. If you plan on accessing data in buckets or the filesystem a lot this way, it is advised to load data into delta or iceberg tables, as DuckDB is able to only load the parts of the data actually needed for the query to work.

tip

By default dlt will not autorefresh views created on iceberg tables and files when new data is loaded. This prevents wasting resources on file globbing and reloading iceberg metadata for every query. You can change this behavior with always_refresh_views flag.

Note: delta tables are by default on autorefresh which is implemented by delta core and seems to be pretty efficient.

Examples

Fetch one record as a tuple

record = customers_relation.fetchone()

Fetch many records as tuples

records = customers_relation.fetchmany(10)

Iterate over data with limit and column selection

Note: When iterating over filesystem tables, the underlying DuckDB may give you a different chunk size depending on the size of the parquet files the table is based on.

# Dataframes
for df_chunk in customers_relation.select("id", "name").limit(100).iter_df(chunk_size=20): ...

# Arrow tables
for arrow_table in (
    customers_relation.select("id", "name").limit(100).iter_arrow(chunk_size=20)
): ...

# Python tuples
for records in customers_relation.select("id", "name").limit(100).iter_fetch(chunk_size=20):
    # Process each modified DataFrame chunk
    ...

Advanced usage

Using custom SQL queries to create ReadableRelations

You can use custom SQL queries directly on the dataset to create a ReadableRelation:

# Join 'customers' and 'purchases' tables
custom_relation = dataset(
    "SELECT * FROM customers JOIN purchases ON customers.id = purchases.customer_id"
)
arrow_table = custom_relation.arrow()

note

When using custom SQL queries with dataset(), methods like limit and select won't work. Include any filtering or column selection directly in your SQL query.

Loading a ReadableRelation into a pipeline table

Since the iter_arrow and iter_df methods are generators that iterate over the full ReadableRelation in chunks, you can use them as a resource for another (or even the same) dlt pipeline:

# Create a readable relation with a limit of 1m rows
limited_customers_relation = dataset.customers.limit(1_000_000)

# Create a new pipeline
other_pipeline = dlt.pipeline(pipeline_name="other_pipeline", destination="duckdb")

# We can now load these 1m rows into this pipeline in 10k chunks
other_pipeline.run(
    limited_customers_relation.iter_arrow(chunk_size=10_000), table_name="limited_customers"
)

Learn more about transforming data in Python with Arrow tables or DataFrames.

Using ibis to query data

Visit the Native Ibis integration guide to learn more.

Important considerations

• Memory usage: Loading full tables into memory without iterating or limiting can consume significant memory, potentially leading to crashes if the dataset is large. Always consider using limits or chunked iteration.

• Lazy evaluation: ReadableDataset and ReadableRelation objects delay data retrieval until necessary. This design improves performance and resource utilization.

• Custom SQL queries: When executing custom SQL queries, remember that additional methods like limit() or select() won't modify the query. Include all necessary clauses directly in your SQL statement.