Read and Write Data¶

Writing Lance Dataset¶

If you’re familiar with Apache PyArrow, you’ll find that creating a Lance dataset is straightforward. Begin by writing a pyarrow.Table using the lance.write_dataset() function.

>>> import lance
>>> import pyarrow as pa

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

If the dataset is too large to fully load into memory, you can stream data using lance.write_dataset() also supports Iterator of pyarrow.RecordBatch es. You will need to provide a pyarrow.Schema for the dataset in this case.

>>> def producer() -> Iterator[pa.RecordBatch]:
...     """An iterator of RecordBatches."""
...     yield pa.RecordBatch.from_pylist([{"name": "Alice", "age": 20}])
...     yield pa.RecordBatch.from_pylist([{"name": "Bob", "age": 30}])

>>> schema = pa.schema([
...     ("name", pa.string()),
...     ("age", pa.int32()),
... ])

>>> ds = lance.write_dataset(producer(),
...                          "./alice_and_bob.lance",
...                          schema=schema, mode="overwrite")
>>> ds.count_rows()
2

lance.write_dataset() supports writing pyarrow.Table, pandas.DataFrame, pyarrow.dataset.Dataset, and Iterator[pyarrow.RecordBatch].

Adding Rows¶

To insert data into your dataset, you can use either LanceDataset.insert or write_dataset() with mode=append.

>>> import lance
>>> import pyarrow as pa

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

>>> new_table = pa.Table.from_pylist([{"name": "Carla", "age": 37}])
>>> ds.insert(new_table)
>>> ds.to_table().to_pandas()
    name  age
0  Alice   20
1    Bob   30
2  Carla   37

>>> new_table2 = pa.Table.from_pylist([{"name": "David", "age": 42}])
>>> ds = lance.write_dataset(new_table2, ds, mode="append")
>>> ds.to_table().to_pandas()
    name  age
0  Alice   20
1    Bob   30
2  Carla   37
3  David   42

Deleting rows¶

Lance supports deleting rows from a dataset using a SQL filter, as described in filter-push-down. 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'")
>>> dataset2 = lance.dataset("./alice_and_bob.lance")
>>> dataset2.to_table().to_pandas()
    name  age
0  Alice   20

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 lance.LanceDataset.merge_insert() operation would be more efficient:

>>> import lance

>>> dataset = lance.dataset("./alice_and_bob.lance")
>>> dataset.to_table().to_pandas()
    name  age
0  Alice   20
1    Bob   30
>>> # Change the ages of both Alice and Bob
>>> new_table = pa.Table.from_pylist([{"name": "Alice", "age": 2},
...                                   {"name": "Bob", "age": 3}])
>>> # 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.
>>> rst = dataset.merge_insert("name") \
...        .when_matched_update_all() \
...        .execute(new_table)
>>> dataset.to_table().to_pandas()
    name  age
0  Alice    2
1    Bob    3

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:

>>> # 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() \
...        .execute(new_table)
>>> # Verify that Carla was added but Bob remains unchanged
>>> dataset.to_table().to_pandas()
    name  age
0  Alice   20
1    Bob   30
2  Carla   37

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
>>> import pyarrow as pa
>>>
>>> # 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() \
...        .execute(new_table)
>>> # Verify the results
>>> dataset.to_table().to_pandas()
    name  age
0  Alice   20
1    Bob   30
2  Carla   27
3  David   42

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
>>> import pyarrow as pa
>>>
>>> new_table = pa.Table.from_pylist([{"name": "Edgar", "age": 46},
...                                   {"name": "Francene", "age": 44}])
>>>
>>> dataset = lance.dataset("./alice_and_bob.lance")
>>> dataset.to_table().to_pandas()
      name  age
0    Alice   20
1      Bob   30
2  Charlie   45
3    Donna   50
>>>
>>> # 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") \
...        .execute(new_table)
>>> # Verify the results - people over 40 replaced with new data
>>> dataset.to_table().to_pandas()
       name  age
0     Alice   20
1       Bob   30
2     Edgar   46
3  Francene   44

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")

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",
    limit=1000,
    offset=3000)

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

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
    compute_on_batch(batch)

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

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

• IS NULL, IS NOT NULL

• IS TRUE, IS NOT TRUE, IS FALSE, IS NOT FALSE

• IN

• LIKE, NOT LIKE

• regexp_match(column, pattern)

• CAST

For example, the following filter string is acceptable:

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

Nested fields can be accessed using the subscripts. Struct fields can be subscripted using field names, while list fields can be subscripted using indices.

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

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.

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

1. See precision mapping in previous table.

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.

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.

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 addresses 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.