Read and Write Lance Dataset

Lance dataset APIs follows the PyArrow API conventions.

Writing Lance Dataset

Similar to Apache Pyarrow, the simplest approach to create a Lance dataset is writing a pyarrow.Table via lance.write_dataset().

import lance
import pyarrow as pa

table = pa.Table.from_pylist([{"name": "Alice", "age": 20},
                              {"name": "Bob", "age": 30}])
lance.write_dataset(table, "./alice_and_bob.lance")

If the memory footprint of the dataset is too large to fit in memory, lance.write_dataset() also supports writing a dataset in iterator of pyarrow.RecordBatch es.

import lance
import pyarrow as pa

def producer():
    yield pa.RecordBatch.from_pylist([{"name": "Alice", "age": 20}])
    yield pa.RecordBatch.from_pylist([{"name": "Blob", "age": 30}])

schema = pa.schema([
        pa.field("name", pa.string()),
        pa.field("age", pa.int64()),

  lance.write_dataset(reader, "./alice_and_bob.lance", schema)

lance.write_dataset() supports writing pyarrow.Table, pandas.DataFrame, pyarrow.Dataset, and Iterator[pyarrow.RecordBatch]. Check its doc for more details.

Adding new columns

New columns can be merged into an existing dataset in using lance.Dataset.merge(). This allows filling in additional columns without having to rewrite the whole dataset.

To use the merge method, provide a new table that includes the columns you want to add, and a column name to use for joining the new data to the existing dataset.

For example, imagine we have a dataset of embeddings and ids:

import lance
import pyarrow as pa
import numpy as np
table = pa.table({
   "id": pa.array([1, 2, 3]),
   "embedding": pa.array([np.array([1, 2, 3]), np.array([4, 5, 6]),
                          np.array([7, 8, 9])])
dataset = lance.write_dataset(table, "embeddings")

Now if we want to add a column of labels we have generated, we can do so by merging a new table:

new_data = pa.table({
   "id": pa.array([1, 2, 3]),
   "label": pa.array(["horse", "rabbit", "cat"])
dataset.merge(new_data, "id")
   id  embedding   label
0   1  [1, 2, 3]   horse
1   2  [4, 5, 6]  rabbit
2   3  [7, 8, 9]     cat

Deleting rows

Lance supports deleting rows from a dataset using a SQL filter. For example, to delete Bob’s row from the dataset above, one could use:

import lance

dataset = lance.dataset("./alice_and_bob.lance")
dataset.delete("name = 'Bob'")

lance.LanceDataset.delete() supports the same filters as described in Filter push-down.

Rows are deleted by marking them as deleted in a separate deletion index. This is faster than rewriting the files and also avoids invaliding any indices that point to those files. Any subsequent queries will not return the deleted rows.


Do not read datasets with deleted rows using Lance versions prior to 0.5.0, as they will return the deleted rows. This is fixed in 0.5.0 and later.

Updating rows

Lance supports updating rows based on SQL expressions with the lance.LanceDataset.update() method. For example, if we notice that Bob’s name in our dataset has been sometimes written as Blob, we can fix that with:

import lance

dataset = lance.dataset("./alice_and_bob.lance")
dataset.update({"name": "'Bob'"}), where="name = 'Blob'")

The update values are SQL expressions, which is why 'Bob' is wrapped in single quotes. This means we can use complex expressions that reference existing columns if we wish. For example, if two years have passed and we wish to update the ages of Alice and Bob in the same example, we could write:

import lance

dataset = lance.dataset("./alice_and_bob.lance")
dataset.update({"age": "age + 2"})

If you are trying to update a set of individual rows with new values then it is often more efficient to use the merge insert operation described below.

import lance

# Change the ages of both Alice and Bob
new_table = pa.Table.from_pylist([{"name": "Alice", "age": 30},
                                  {"name": "Bob", "age": 20}])

# This works, but is inefficient, see below for a better approach
dataset = lance.dataset("./alice_and_bob.lance")
for idx in range(new_table.num_rows):
  name = new_table[0][idx].as_py()
  new_age = new_table[1][idx].as_py()
  dataset.update({"age": new_age}, where=f"name='{name}'")

Merge Insert

Lance supports a merge insert operation. This can be used to add new data in bulk while also (potentially) matching against existing data. This operation can be used for a number of different use cases.

Bulk Update

The lance.LanceDataset.update() method is useful for updating rows based on a filter. However, if we want to replace existing rows with new rows then a merge insert operation would be more efficient:

import lance

# Change the ages of both Alice and Bob
new_table = pa.Table.from_pylist([{"name": "Alice", "age": 30},
                                  {"name": "Bob", "age": 20}])
dataset = lance.dataset("./alice_and_bob.lance")
# This will use `name` as the key for matching rows.  Merge insert
# uses a JOIN internally and so you typically want this column to
# be a unique key or id of some kind.
dataset.merge_insert("name") \
       .when_matched_update_all() \

Note that, similar to the update operation, rows that are modified will be removed and inserted back into the table, changing their position to the end. Also, the relative order of these rows could change because we are using a hash-join operation internally.

Insert if not Exists

Sometimes we only want to insert data if we haven’t already inserted it before. This can happen, for example, when we have a batch of data but we don’t know which rows we’ve added previously and we don’t want to create duplicate rows. We can use the merge insert operation to achieve this:

import lance

# Bob is already in the table, but Carla is new
new_table = pa.Table.from_pylist([{"name": "Bob", "age": 30},
                                  {"name": "Carla", "age": 37}])

dataset = lance.dataset("./alice_and_bob.lance")

# This will insert Carla but leave Bob unchanged
dataset.merge_insert("name") \
       .when_not_matched_insert_all() \

Update or Insert (Upsert)

Sometimes we want to combine both of the above behaviors. If a row already exists we want to update it. If the row does not exist we want to add it. This operation is sometimes called “upsert”. We can use the merge insert operation to do this as well:

import lance

# Change Carla's age and insert David
new_table = pa.Table.from_pylist([{"name": "Carla", "age": 27},
                                  {"name": "David", "age": 42}])

dataset = lance.dataset("./alice_and_bob.lance")

# This will update Carla and insert David
dataset.merge_insert("name") \
       .when_matched_update_all() \
       .when_not_matched_insert_all() \

Replace a Portion of Data

A less common, but still useful, behavior can be to replace some region of existing rows (defined by a filter) with new data. This is similar to performing both a delete and an insert in a single transaction. For example:

import lance

new_table = pa.Table.from_pylist([{"name": "Edgar", "age": 46},
                                  {"name": "Francene", "age": 44}])

dataset = lance.dataset("./alice_and_bob.lance")

# This will remove anyone above 40 and insert our new data
dataset.merge_insert("name") \
       .when_not_matched_insert_all() \
       .when_not_matched_by_source_delete("age >= 40") \

Reading Lance Dataset

To open a Lance dataset, use the lance.dataset() function:

import lance
ds = lance.dataset("s3://bucket/path/imagenet.lance")
# Or local path
ds = lance.dataset("./imagenet.lance")


Lance supports local file system, AWS s3 and Google Cloud Storage(gs) as storage backends at the moment. Read more in Object Store Configuration.

The most straightforward approach for reading a Lance dataset is to utilize the lance.LanceDataset.to_table() method in order to load the entire dataset into memory.

table = ds.to_table()

Due to Lance being a high-performance columnar format, it enables efficient reading of subsets of the dataset by utilizing Column (projection) push-down and filter (predicates) push-downs.

table = ds.to_table(
    columns=["image", "label"],
    filter="label = 2 AND text IS NOT NULL",

Lance understands the cost of reading heavy columns such as image. Consequently, it employs an optimized query plan to execute the operation efficiently.

Iterative Read

If the dataset is too large to fit in memory, you can read it in batches using the lance.LanceDataset.to_batches() method:

for batch in ds.to_batches(columns=["image"], filter="label = 10"):
    # do something with batch

Unsurprisingly, to_batches() takes the same parameters as to_table() function.

Filter push-down

Lance embraces the utilization of standard SQL expressions as predicates for dataset filtering. By pushing down the SQL predicates directly to the storage system, the overall I/O load during a scan is significantly reduced.

Currently, Lance supports a growing list of expressions.

  • >, >=, <, <=, =

  • AND, OR, NOT



  • IN


  • regexp_match(column, pattern)

  • CAST

For example, the following filter string is acceptable:

((label IN [10, 20]) AND ( IS NOT NULL))
    OR NOT note.created

If your column name contains special characters or is a SQL Keyword, you can use backtick (`) to escape it. For nested fields, each segment of the path must be wrapped in backticks.

`CUBE` = 10 AND `column name with space` IS NOT NULL
  AND `nested with space`.`inner with space` < 2


Field names containing periods (.) are not supported.

Literals for dates, timestamps, and decimals can be written by writing the string value after the type name. For example

date_col = date '2021-01-01'
and timestamp_col = timestamp '2021-01-01 00:00:00'
and decimal_col = decimal(8,3) '1.000'

For timestamp columns, the precision can be specified as a number in the type parameter. Microsecond precision (6) is the default.


Time unit









Lance internally stores data in Arrow format. The mapping from SQL types to Arrow is:

SQL type

Arrow type



tinyint / tinyint unsigned

Int8 / UInt8

smallint / smallint unsigned

Int16 / UInt16

int or integer / int unsigned or integer unsigned

Int32 / UInt32

bigint / bigint unsigned

Int64 / UInt64





decimal(precision, scale)





Timestamp (1)





  1. See precision mapping in previous table.

Random read

One district feature of Lance, as columnar format, is that it allows you to read random samples quickly.

# Access the 2nd, 101th and 501th rows
data = ds.take([1, 100, 500], columns=["image", "label"])

The ability to achieve fast random access to individual rows plays a crucial role in facilitating various workflows such as random sampling and shuffling in ML training. Additionally, it empowers users to construct secondary indices, enabling swift execution of queries for enhanced performance.

Table Maintenance

Some operations over time will cause a Lance dataset to have a poor layout. For example, many small appends will lead to a large number of small fragments. Or deleting many rows will lead to slower queries due to the need to filter out deleted rows.

To address this, Lance provides methods for optimizing dataset layout.

Compact data files

Data files can be rewritten so there are fewer files. When passing a target_rows_per_fragment to lance.dataset.DatasetOptimizer.compact_files(), Lance will skip any fragments that are already above that row count, and rewrite others. Fragments will be merged according to their fragment ids, so the inherent ordering of the data will be preserved.


Compaction creates a new version of the table. It does not delete the old version of the table and the files referenced by it.

import lance

dataset = lance.dataset("./alice_and_bob.lance")
dataset.optimize.compact_files(target_rows_per_fragment=1024 * 1024)

During compaction, Lance can also remove deleted rows. Rewritten fragments will not have deletion files. This can improve scan performance since the soft deleted rows don’t have to be skipped during the scan.

When files are rewritten, the original row ids are invalidated. This means the affected files are no longer part of any ANN index if they were before. Because of this, it’s recommended to rewrite files before re-building indices.

Object Store Configuration

Lance supports object stores such as AWS S3 (and compatible stores), Azure Blob Store, and Google Cloud Storage. Which object store to use is determined by the URI scheme of the dataset path. For example, s3://bucket/path will use S3, az://bucket/path will use Azure, and gs://bucket/path will use GCS.

Lance uses the object-store Rust crate for object store access. There are general environment variables that can be used to configure the object store, such as the request timeout and proxy configuration. See the object_store ClientConfigKey docs for available configuration options. (The environment variables that can be set are the snake-cased versions of these variable names. For example, to set ProxyUrl use the environment variable PROXY_URL.)

S3 Configuration

To configure credentials for AWS S3, you can use the AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_SESSION_TOKEN environment variables.

Alternatively, if you are using AWS SSO, you can use the AWS_PROFILE and AWS_DEFAULT_REGION environment variables.

You can see a full list of environment variables here.

S3-compatible stores

Lance can also connect to S3-compatible stores, such as MinIO. To do so, you must specify two environment variables: AWS_ENDPOINT and AWS_DEFAULT_REGION. AWS_ENDPOINT should be the URL of the S3-compatible store, and AWS_DEFAULT_REGION should be the region to use.

S3 Express

New in version 0.9.7.

Lance supports S3 Express One Zone endpoints, but requires additional configuration. Also, S3 Express endpoints only support connecting from an EC2 instance within the same region.

To configure Lance to use an S3 Express endpoint, you must set the environment variable S3_EXPRESS:

export S3_EXPRESS=true

You can then pass the bucket name including the suffix as you would normally:

import lance
ds = lance.dataset("s3://my-bucket--use1-az4--x-s3/path/imagenet.lance")

Committing mechanisms for S3

Most supported storage systems (e.g. local file system, Google Cloud Storage, Azure Blob Store) natively support atomic commits, which prevent concurrent writers from corrupting the dataset. However, S3 does not support this natively. To work around this, you may provide a locking mechanism that Lance can use to lock the table while providing a write. To do so, you should implement a context manager that acquires and releases a lock and then pass that to the commit_lock parameter of lance.write_dataset().


In order for the locking mechanism to work, all writers must use the same exact mechanism. Otherwise, Lance will not be able to detect conflicts.

On entering, the context manager should acquire the lock on the table. The table version being committed is passed in as an argument, which may be used if the locking service wishes to keep track of the current version of the table, but this is not required. If the table is already locked by another transaction, it should wait until it is unlocked, since the other transaction may fail. Once unlocked, it should either lock the table or, if the lock keeps track of the current version of the table, return a CommitConflictError if the requested version has already been committed.

To prevent poisoned locks, it’s recommended to set a timeout on the locks. That way, if a process crashes while holding the lock, the lock will be released eventually. The timeout should be no less than 30 seconds.

from contextlib import contextmanager

def commit_lock(version: int);
    # Acquire the lock
      failed = True

lance.write_dataset(data, "s3://bucket/path/", commit_lock=commit_lock)

When the context manager is exited, it will raise an exception if the commit failed. This might be because of a network error or if the version has already been written. Either way, the context manager should release the lock. Use a try/finally block to ensure that the lock is released.

Concurrent Writer on S3 using DynamoDB


This feature is experimental at the moment

Lance has native support for concurrent writers on S3 using DynamoDB instead of locking. User may pass in a DynamoDB table name alone with the S3 URI to their dataset to enable this feature.

import lance
# s3+ddb:// URL scheme let's lance know that you want to use DynamoDB for writing to S3 concurrently
ds = lance.dataset("s3+ddb://my-bucket/mydataset.lance?ddbTableName=mytable")

The DynamoDB table is expected to have a primary hash key of base_uri and a range key version. The key base_uri should be string type, and the key version should be number type.

For details on how this feature works, please see External Manifest Store.

Google Cloud Storage Configuration

GCS credentials are configured by setting the GOOGLE_SERVICE_ACCOUNT environment variable to the path of a JSON file containing the service account credentials. There are several aliases for this environment variable, documented here.


By default, GCS uses HTTP/1 for communication, as opposed to HTTP/2. This improves maximum throughput significantly. However, if you wish to use HTTP/2 for some reason, you can set the environment variable HTTP1_ONLY to false.

Azure Blob Storage Configuration

Azure Blob Storage credentials can be configured by setting the AZURE_STORAGE_ACCOUNT_NAME and AZURE_STORAGE_ACCOUNT_KEY environment variables. The full list of environment variables that can be set are documented here.