A DeltaTable represents the state of a delta table at a particular version. This includes which files are currently part of the table, the schema of the table, and other metadata such as creation time.

>>> from deltalake import DeltaTable
>>> dt = DeltaTable("../rust/tests/data/delta-0.2.0")
>>> dt.version()
>>> dt.files()

Loading a Delta Table

To load the current version, use the constructor:

>>> dt = DeltaTable("../rust/tests/data/delta-0.2.0")

Depending on your storage backend, you could use the storage_options parameter to provide some configuration. Configuration is defined for specific backends - s3 options, azure options, gcs options.

>>> dt = DeltaTable("../rust/tests/data/delta-0.2.0", storage_options=storage_options)

The configuration can also be provided via the environment, and the basic service provider is derived from the URL being used. We try to support many of the well-known formats to identify basic service properties.


  • s3://<bucket>/<path>

  • s3a://<bucket>/<path>


  • az://<container>/<path>

  • adl://<container>/<path>

  • abfs://<container>/<path>


  • gs://<bucket>/<path>

Alternatively, if you have a data catalog you can load it by reference to a database and table name. Currently supported are AWS Glue and Databricks Unity Catalog.

For AWS Glue catalog, use AWS environment variables to authenticate.

>>> from deltalake import DeltaTable
>>> from deltalake import DataCatalog
>>> database_name = "simple_database"
>>> table_name = "simple_table"
>>> data_catalog = DataCatalog.AWS
>>> dt = DeltaTable.from_data_catalog(data_catalog=data_catalog, database_name=database_name, table_name=table_name)
>>> dt.to_pyarrow_table().to_pydict()
{'id': [5, 7, 9, 5, 6, 7, 8, 9]}
For Databricks Unity Catalog authentication, use environment variables:
>>> import os
>>> from deltalake import DataCatalog, DeltaTable
>>> os.environ['DATABRICKS_WORKSPACE_URL'] = ""
>>> os.environ['DATABRICKS_ACCESS_TOKEN'] = "<DBAT>"
>>> catalog_name = 'main'
>>> schema_name = 'db_schema'
>>> table_name = 'db_table'
>>> data_catalog = DataCatalog.UNITY
>>> dt = DeltaTable.from_data_catalog(data_catalog=data_catalog, data_catalog_id=catalog_name, database_name=schema_name, table_name=table_name)

Custom Storage Backends

While delta always needs its internal storage backend to work and be properly configured, in order to manage the delta log, it may sometime be advantageous - and is common practice in the arrow world - to customize the storage interface used for reading the bulk data.

deltalake will work with any storage compliant with pyarrow.fs.FileSystem, however the root of the filesystem has to be adjusted to point at the root of the Delta table. We can achieve this by wrapping the custom filesystem into a pyarrow.fs.SubTreeFileSystem.

import pyarrow.fs as fs
from deltalake import DeltaTable

path = "<path/to/table>"
filesystem = fs.SubTreeFileSystem(path, fs.LocalFileSystem())

dt = DeltaTable(path)
ds = dt.to_pyarrow_dataset(filesystem=filesystem)

When using the pyarrow factory method for file systems, the normalized path is provided on creation. In case of S3 this would look something like:

import pyarrow.fs as fs
from deltalake import DeltaTable

table_uri = "s3://<bucket>/<path>"
raw_fs, normalized_path = fs.FileSystem.from_uri(table_uri)
filesystem = fs.SubTreeFileSystem(normalized_path, raw_fs)

dt = DeltaTable(table_uri)
ds = dt.to_pyarrow_dataset(filesystem=filesystem)

Time Travel

To load previous table states, you can provide the version number you wish to load:

>>> dt = DeltaTable("../rust/tests/data/simple_table", version=2)

Once you’ve loaded a table, you can also change versions using either a version number or datetime string:

>>> dt.load_version(1)
>>> dt.load_with_datetime("2021-11-04 00:05:23.283+00:00")


Previous table versions may not exist if they have been vacuumed, in which case an exception will be thrown. See Vacuuming tables for more information.

Examining a Table


The delta log maintains basic metadata about a table, including:

  • A unique id

  • A name, if provided

  • A description, if provided

  • The list of partition_columns.

  • The created_time of the table

  • A map of table configuration. This includes fields such as delta.appendOnly, which if true indicates the table is not meant to have data deleted from it.

Get metadata from a table with the DeltaTable.metadata() method:

>>> from deltalake import DeltaTable
>>> dt = DeltaTable("../rust/tests/data/simple_table")
>>> dt.metadata()
Metadata(id: 5fba94ed-9794-4965-ba6e-6ee3c0d22af9, name: None, description: None, partitionColumns: [], created_time: 1587968585495, configuration={})


The schema for the table is also saved in the transaction log. It can either be retrieved in the Delta Lake form as deltalake.schema.Schema or as a PyArrow schema. The first allows you to introspect any column-level metadata stored in the schema, while the latter represents the schema the table will be loaded into.

Use DeltaTable.schema() to retrieve the delta lake schema:

>>> from deltalake import DeltaTable
>>> dt = DeltaTable("../rust/tests/data/simple_table")
>>> dt.schema()
Schema([Field(id, PrimitiveType("long"), nullable=True)])

These schemas have a JSON representation that can be retrieved. To reconstruct from json, use deltalake.schema.Schema.from_json().

>>> dt.schema().json()

Use deltalake.schema.Schema.to_pyarrow() to retrieve the PyArrow schema:

>>> dt.schema().to_pyarrow()
id: int64


Depending on what system wrote the table, the delta table may have provenance information describing what operations were performed on the table, when, and by whom. This information is retained for 30 days by default, unless otherwise specified by the table configuration delta.logRetentionDuration.


This information is not written by all writers and different writers may use different schemas to encode the actions. For Spark’s format, see:

To view the available history, use DeltaTable.history():

>>> from deltalake import DeltaTable
>>> dt = DeltaTable("../rust/tests/data/simple_table")
>>> dt.history()
[{'timestamp': 1587968626537, 'operation': 'DELETE', 'operationParameters': {'predicate': '["((`id` % CAST(2 AS BIGINT)) = CAST(0 AS BIGINT))"]'}, 'readVersion': 3, 'isBlindAppend': False},
 {'timestamp': 1587968614187, 'operation': 'UPDATE', 'operationParameters': {'predicate': '((id#697L % cast(2 as bigint)) = cast(0 as bigint))'}, 'readVersion': 2, 'isBlindAppend': False},
 {'timestamp': 1587968604143, 'operation': 'WRITE', 'operationParameters': {'mode': 'Overwrite', 'partitionBy': '[]'}, 'readVersion': 1, 'isBlindAppend': False},
 {'timestamp': 1587968596254, 'operation': 'MERGE', 'operationParameters': {'predicate': '(oldData.`id` = newData.`id`)'}, 'readVersion': 0, 'isBlindAppend': False},
 {'timestamp': 1587968586154, 'operation': 'WRITE', 'operationParameters': {'mode': 'ErrorIfExists', 'partitionBy': '[]'}, 'isBlindAppend': True}]

Current Add Actions

The active state for a delta table is determined by the Add actions, which provide the list of files that are part of the table and metadata about them, such as creation time, size, and statistics. You can get a data frame of the add actions data using DeltaTable.get_add_actions():

>>> from deltalake import DeltaTable
>>> dt = DeltaTable("../rust/tests/data/delta-0.8.0")
>>> dt.get_add_actions(flatten=True).to_pandas()
                                                    path  size_bytes   modification_time  data_change  num_records  null_count.value  min.value  max.value
0  part-00000-c9b90f86-73e6-46c8-93ba-ff6bfaf892a...         440 2021-03-06 15:16:07         True            2                 0          0          2
1  part-00000-04ec9591-0b73-459e-8d18-ba5711d6cbe...         440 2021-03-06 15:16:16         True            2                 0          2          4

This works even with past versions of the table:

>>> dt = DeltaTable("../rust/tests/data/delta-0.8.0", version=0)
>>> dt.get_add_actions(flatten=True).to_pandas()
                                                path  size_bytes   modification_time  data_change  num_records  null_count.value  min.value  max.value
0  part-00000-c9b90f86-73e6-46c8-93ba-ff6bfaf892a...         440 2021-03-06 15:16:07         True            2                 0          0          2
1  part-00001-911a94a2-43f6-4acb-8620-5e68c265498...         445 2021-03-06 15:16:07         True            3                 0          2          4

Querying Delta Tables

Delta tables can be queried in several ways. By loading as Arrow data or an Arrow dataset, they can be used by compatible engines such as Pandas and DuckDB. By passing on the list of files, they can be loaded into other engines such as Dask.

Delta tables are often larger than can fit into memory on a single computer, so this module provides ways to read only the parts of the data you need. Partition filters allow you to skip reading files that are part of irrelevant partitions. Only loading the columns required also saves memory. Finally, some methods allow reading tables batch-by-batch, allowing you to process the whole table while only having a portion loaded at any given time.

To load into Pandas or a PyArrow table use the DeltaTable.to_pandas() and DeltaTable.to_pyarrow_table() methods, respectively. Both of these support filtering partitions and selecting particular columns.

>>> from deltalake import DeltaTable
>>> dt = DeltaTable("../rust/tests/data/delta-0.8.0-partitioned")
>>> dt.schema().to_pyarrow()
value: string
year: string
month: string
day: string
>>> dt.to_pandas(partitions=[("year", "=", "2021")], columns=["value"])
0     6
1     7
2     5
3     4
>>> dt.to_pyarrow_table(partitions=[("year", "=", "2021")], columns=["value"])
value: string

Converting to a PyArrow Dataset allows you to filter on columns other than partition columns and load the result as a stream of batches rather than a single table. Convert to a dataset using DeltaTable.to_pyarrow_dataset(). Filters applied to datasets will use the partition values and file statistics from the Delta transaction log and push down any other filters to the scanning operation.

>>> import pyarrow.dataset as ds
>>> dataset = dt.to_pyarrow_dataset()
>>> condition = (ds.field("year") == "2021") & (ds.field("value") > "4")
>>> dataset.to_table(filter=condition, columns=["value"]).to_pandas()
0     6
1     7
2     5
>>> batch_iter = dataset.to_batches(filter=condition, columns=["value"], batch_size=2)
>>> for batch in batch_iter: print(batch.to_pandas())
0     6
1     7
0     5

PyArrow datasets may also be passed to compatible query engines, such as DuckDB.

>>> import duckdb
>>> ex_data = duckdb.arrow(dataset)
>>> ex_data.filter("year = 2021 and value > 4").project("value")
-- Expression Tree --
Projection [value]
  Filter [year=2021 AND value>4]
    arrow_scan(140409099470144, 4828104688, 1000000)

-- Result Columns  --
- value (VARCHAR)

-- Result Preview  --
[ Rows: 3]

Finally, you can always pass the list of file paths to an engine. For example, you can pass them to dask.dataframe.read_parquet:

>>> import dask.dataframe as dd
>>> df = dd.read_parquet(dt.file_uris())
>>> df
Dask DataFrame Structure:
                value             year            month              day
               object  category[known]  category[known]  category[known]
                  ...              ...              ...              ...
...               ...              ...              ...              ...
                  ...              ...              ...              ...
                  ...              ...              ...              ...
Dask Name: read-parquet, 6 tasks
>>> df.compute()
  value  year month day
0     1  2020     1   1
0     2  2020     2   3
0     3  2020     2   5
0     4  2021     4   5
0     5  2021    12   4
0     6  2021    12  20
1     7  2021    12  20

Managing Delta Tables

Vacuuming tables

Vacuuming a table will delete any files that have been marked for deletion. This may make some past versions of a table invalid, so this can break time travel. However, it will save storage space. Vacuum will retain files in a certain window, by default one week, so time travel will still work in shorter ranges.

Delta tables usually don’t delete old files automatically, so vacuuming regularly is considered good practice, unless the table is only appended to.

Use DeltaTable.vacuum() to perform the vacuum operation. Note that to prevent accidental deletion, the function performs a dry-run by default: it will only list the files to be deleted. Pass dry_run=False to actually delete files.

>>> dt = DeltaTable("../rust/tests/data/simple_table")
>>> dt.vacuum()
>>> dt.vacuum(dry_run=False) # Don't run this unless you are sure!

Optimizing tables

Optimizing a table will perform bin-packing on a Delta Table which merges small files into a large file. Bin-packing reduces the number of API calls required for read operations. Optimizing will increments the table’s version and creates remove actions for optimized files. Optimize does not delete files from storage. To delete files that were removed, call DeltaTable.vacuum().

DeltaTable.optimize returns a TableOptimizer object which provides methods for optimizing the table. Note that these method will fail if a concurrent writer performs an operation that removes any files (such as an overwrite).

For just file compaction, use the TableOptimizer.compact() method:

>>> dt = DeltaTable("../rust/tests/data/simple_table")
>>> dt.optimize.compact()
{'numFilesAdded': 1, 'numFilesRemoved': 5,
 'filesAdded': {'min': 555, 'max': 555, 'avg': 555.0, 'totalFiles': 1, 'totalSize': 555},
 'filesRemoved': {'min': 262, 'max': 429, 'avg': 362.2, 'totalFiles': 5, 'totalSize': 1811},
 'partitionsOptimized': 1, 'numBatches': 1, 'totalConsideredFiles': 5,
 'totalFilesSkipped': 0, 'preserveInsertionOrder': True}

For improved data skipping, use the TableOptimizer.z_order() method. This is slower than just file compaction, but can improve performance for queries that filter on multiple columns at once.

>>> dt = DeltaTable("../rust/tests/data/COVID-19_NYT")
>>> dt.optimize.z_order(["date", "county"])
{'numFilesAdded': 1, 'numFilesRemoved': 8,
 'filesAdded': {'min': 2473439, 'max': 2473439, 'avg': 2473439.0, 'totalFiles': 1, 'totalSize': 2473439},
 'filesRemoved': {'min': 325440, 'max': 895702, 'avg': 773810.625, 'totalFiles': 8, 'totalSize': 6190485},
 'partitionsOptimized': 0, 'numBatches': 1, 'totalConsideredFiles': 8,
 'totalFilesSkipped': 0, 'preserveInsertionOrder': True}

Writing Delta Tables

For overwrites and appends, use write_deltalake(). If the table does not already exist, it will be created. The data parameter will accept a Pandas DataFrame, a PyArrow Table, or an iterator of PyArrow Record Batches.

>>> from deltalake import write_deltalake
>>> df = pd.DataFrame({'x': [1, 2, 3]})
>>> write_deltalake('path/to/table', df)


write_deltalake() accepts a Pandas DataFrame, but will convert it to a Arrow table before writing. See caveats in Pandas Integration.

By default, writes create a new table and error if it already exists. This is controlled by the mode parameter, which mirrors the behavior of Spark’s pyspark.sql.DataFrameWriter.saveAsTable() DataFrame method. To overwrite pass in mode='overwrite' and to append pass in mode='append':

>>> write_deltalake('path/to/table', df, mode='overwrite')
>>> write_deltalake('path/to/table', df, mode='append')

write_deltalake() will raise ValueError if the schema of the data passed to it differs from the existing table’s schema. If you wish to alter the schema as part of an overwrite pass in overwrite_schema=True.

Updating Delta Tables

Row values in an existing delta table can be updated with the DeltaTable.update() command. A update dictionary has to be passed, where they key is the column you wish to update, and the value is a Expression in string format.

Update all the rows for the column “processed” to the value True.

>>> from deltalake import write_deltalake, DeltaTable
>>> df = pd.DataFrame({'x': [1, 2, 3], 'deleted': [False, False, False]})
>>> write_deltalake('path/to/table', df)
>>> dt = DeltaTable('path/to/table')
>>> dt.update({"processed": "True"})
>>> dt.to_pandas()
>>>     x       processed
0       1       True
1       2       True
2       3       True


DeltaTable.update() predicates and updates are all in string format. The predicates and expressions, are parsed into Apache Datafusion expressions.

Apply a soft deletion based on a predicate, so update all the rows for the column “deleted” to the value True where x = 3

>>> from deltalake import write_deltalake, DeltaTable
>>> df = pd.DataFrame({'x': [1, 2, 3], 'deleted': [False, False, False]})
>>> write_deltalake('path/to/table', df)
>>> dt = DeltaTable('path/to/table')
>>> dt.update(
...    updates={"deleted": "True"},
...    predicate= 'x = 3',
... )
>>> dt.to_pandas()
>>>     x       deleted
0       1       False
1       2       False
2       3       True

Overwriting a partition

You can overwrite a specific partition by using mode="overwrite" together with partition_filters. This will remove all files within the matching partition and insert your data as new files. This can only be done on one partition at a time. All of the input data must belong to that partition or else the method will raise an error.

>>> from deltalake import write_deltalake
>>> df = pd.DataFrame({'x': [1, 2, 3], 'y': ['a', 'a', 'b']})
>>> write_deltalake('path/to/table', df, partition_by=['y'])

>>> table = DeltaTable('path/to/table')
>>> df2 = pd.DataFrame({'x': [100], 'y': ['b']})
>>> write_deltalake(table, df2, partition_filters=[('y', '=', 'b')], mode="overwrite")

>>> table.to_pandas()
     x  y
0    1  a
1    2  a
2  100  b

This method could also be used to insert a new partition if one doesn’t already exist, making this operation idempotent.

Removing data

You can remove rows from a table with DeltaTable.delete(). A SQL where clause can be provided to only remove some rows. If the clause matches some partition values, then the files under those partition values will be removed. If the clause matches rows inside some files, then those files will rewritten without the matched rows. Omitting the clause will remove all files from the table.

>>> from deltalake import DeltaTable, write_deltalake
>>> df = pd.DataFrame({'a': [1, 2, 3], 'to_delete': [False, False, True]})
>>> write_deltalake('path/to/table', df)

>>> table = DeltaTable('path/to/table')
>>> table.delete(predicate="to_delete = true")
{'num_added_files': 1, 'num_removed_files': 1, 'num_deleted_rows': 1, 'num_copied_rows': 2, 'execution_time_ms': 11081, 'scan_time_ms': 3721, 'rewrite_time_ms': 7}

>>> table.to_pandas()
   a  to_delete
0  1      False
1  2      False


DeltaTable.delete() does not delete files from storage but only updates the table state to one where the deleted rows are no longer present. See Vacuuming tables for more information.

Restoring tables

Restoring a table will restore delta table to a specified version or datetime. This operation compares the current state of the delta table with the state to be restored. And add those missing files into the AddFile actions and add redundant files into RemoveFile actions. Then commit into a new version.

Use DeltaTable.restore() to perform the restore operation. Note that if any other concurrent operation was performed on the table, restore will fail.

>>> dt = DeltaTable("../rust/tests/data/simple_table")
>>> dt.restore(1)
{'numRemovedFile': 5, 'numRestoredFile': 22}