Python API¶
PyIceberg is based around catalogs to load tables. First step is to instantiate a catalog that loads tables. Let's use the following configuration to define a catalog called prod:
Note that multiple catalogs can be defined in the same .pyiceberg.yaml:
catalog:
hive:
uri: thrift://127.0.0.1:9083
s3.endpoint: http://127.0.0.1:9000
s3.access-key-id: admin
s3.secret-access-key: password
rest:
uri: https://rest-server:8181/
warehouse: my-warehouse
and loaded in python by calling load_catalog(name="hive") and load_catalog(name="rest").
This information must be placed inside a file called .pyiceberg.yaml located either in the $HOME or %USERPROFILE% directory (depending on whether the operating system is Unix-based or Windows-based, respectively) or in the $PYICEBERG_HOME directory (if the corresponding environment variable is set).
For more details on possible configurations refer to the specific page.
Then load the prod catalog:
from pyiceberg.catalog import load_catalog
catalog = load_catalog(
"docs",
**{
"uri": "http://127.0.0.1:8181",
"s3.endpoint": "http://127.0.0.1:9000",
"py-io-impl": "pyiceberg.io.pyarrow.PyArrowFileIO",
"s3.access-key-id": "admin",
"s3.secret-access-key": "password",
}
)
Let's create a namespace:
And then list them:
And then list tables in the namespace:
Create a table¶
To create a table from a catalog:
from pyiceberg.schema import Schema
from pyiceberg.types import (
TimestampType,
FloatType,
DoubleType,
StringType,
NestedField,
StructType,
)
schema = Schema(
NestedField(field_id=1, name="datetime", field_type=TimestampType(), required=True),
NestedField(field_id=2, name="symbol", field_type=StringType(), required=True),
NestedField(field_id=3, name="bid", field_type=FloatType(), required=False),
NestedField(field_id=4, name="ask", field_type=DoubleType(), required=False),
NestedField(
field_id=5,
name="details",
field_type=StructType(
NestedField(
field_id=4, name="created_by", field_type=StringType(), required=False
),
),
required=False,
),
)
from pyiceberg.partitioning import PartitionSpec, PartitionField
from pyiceberg.transforms import DayTransform
partition_spec = PartitionSpec(
PartitionField(
source_id=1, field_id=1000, transform=DayTransform(), name="datetime_day"
)
)
from pyiceberg.table.sorting import SortOrder, SortField
from pyiceberg.transforms import IdentityTransform
# Sort on the symbol
sort_order = SortOrder(SortField(source_id=2, transform=IdentityTransform()))
catalog.create_table(
identifier="docs_example.bids",
schema=schema,
location="s3://pyiceberg",
partition_spec=partition_spec,
sort_order=sort_order,
)
When the table is created, all IDs in the schema are re-assigned to ensure uniqueness.
To create a table using a pyarrow schema:
import pyarrow as pa
schema = pa.schema(
[
pa.field("foo", pa.string(), nullable=True),
pa.field("bar", pa.int32(), nullable=False),
pa.field("baz", pa.bool_(), nullable=True),
]
)
catalog.create_table(
identifier="docs_example.bids",
schema=schema,
)
To create a table with some subsequent changes atomically in a transaction:
with catalog.create_table_transaction(
identifier="docs_example.bids",
schema=schema,
location="s3://pyiceberg",
partition_spec=partition_spec,
sort_order=sort_order,
) as txn:
with txn.update_schema() as update_schema:
update_schema.add_column(path="new_column", field_type=StringType())
with txn.update_spec() as update_spec:
update_spec.add_identity("symbol")
txn.set_properties(test_a="test_aa", test_b="test_b", test_c="test_c")
Load a table¶
Catalog table¶
Loading the bids table:
table = catalog.load_table("docs_example.bids")
# Equivalent to:
table = catalog.load_table(("docs_example", "bids"))
# The tuple syntax can be used if the namespace or table contains a dot.
This returns a Table that represents an Iceberg table that can be queried and altered.
Static table¶
To load a table directly from a metadata file (i.e., without using a catalog), you can use a StaticTable as follows:
from pyiceberg.table import StaticTable
static_table = StaticTable.from_metadata(
"s3://warehouse/wh/nyc.db/taxis/metadata/00002-6ea51ce3-62aa-4197-9cf8-43d07c3440ca.metadata.json"
)
The static-table is considered read-only.
Check if a table exists¶
To check whether the bids table exists:
Returns True if the table already exists.
Write support¶
With PyIceberg 0.6.0 write support is added through Arrow. Let's consider an Arrow Table:
import pyarrow as pa
df = pa.Table.from_pylist(
[
{"city": "Amsterdam", "lat": 52.371807, "long": 4.896029},
{"city": "San Francisco", "lat": 37.773972, "long": -122.431297},
{"city": "Drachten", "lat": 53.11254, "long": 6.0989},
{"city": "Paris", "lat": 48.864716, "long": 2.349014},
],
)
Next, create a table based on the schema:
from pyiceberg.catalog import load_catalog
catalog = load_catalog("default")
from pyiceberg.schema import Schema
from pyiceberg.types import NestedField, StringType, DoubleType
schema = Schema(
NestedField(1, "city", StringType(), required=False),
NestedField(2, "lat", DoubleType(), required=False),
NestedField(3, "long", DoubleType(), required=False),
)
tbl = catalog.create_table("default.cities", schema=schema)
Now write the data to the table:
Fast append
PyIceberg default to the fast append to minimize the amount of data written. This enables quick writes, reducing the possibility of conflicts. The downside of the fast append is that it creates more metadata than a normal commit. Compaction is planned and will automatically rewrite all the metadata when a threshold is hit, to maintain performant reads.
The data is written to the table, and when the table is read using tbl.scan().to_arrow():
pyarrow.Table
city: string
lat: double
long: double
----
city: [["Amsterdam","San Francisco","Drachten","Paris"]]
lat: [[52.371807,37.773972,53.11254,48.864716]]
long: [[4.896029,-122.431297,6.0989,2.349014]]
You both can use append(df) or overwrite(df) since there is no data yet. If we want to add more data, we can use .append() again:
df = pa.Table.from_pylist(
[{"city": "Groningen", "lat": 53.21917, "long": 6.56667}],
)
tbl.append(df)
When reading the table tbl.scan().to_arrow() you can see that Groningen is now also part of the table:
pyarrow.Table
city: string
lat: double
long: double
----
city: [["Amsterdam","San Francisco","Drachten","Paris"],["Groningen"]]
lat: [[52.371807,37.773972,53.11254,48.864716],[53.21917]]
long: [[4.896029,-122.431297,6.0989,2.349014],[6.56667]]
The nested lists indicate the different Arrow buffers, where the first write results into a buffer, and the second append in a separate buffer. This is expected since it will read two parquet files.
To avoid any type errors during writing, you can enforce the PyArrow table types using the Iceberg table schema:
from pyiceberg.catalog import load_catalog
import pyarrow as pa
catalog = load_catalog("default")
table = catalog.load_table("default.cities")
schema = table.schema().as_arrow()
df = pa.Table.from_pylist(
[{"city": "Groningen", "lat": 53.21917, "long": 6.56667}], schema=schema
)
table.append(df)
You can delete some of the data from the table by calling tbl.delete() with a desired delete_filter.
In the above example, any records where the city field value equals to Paris will be deleted.
Running tbl.scan().to_arrow() will now yield:
pyarrow.Table
city: string
lat: double
long: double
----
city: [["Amsterdam","San Francisco","Drachten"],["Groningen"]]
lat: [[52.371807,37.773972,53.11254],[53.21917]]
long: [[4.896029,-122.431297,6.0989],[6.56667]]
Inspecting tables¶
To explore the table metadata, tables can be inspected.
Time Travel
To inspect a tables's metadata with the time travel feature, call the inspect table method with the snapshot_id argument.
Time travel is supported on all metadata tables except snapshots and refs.
Snapshots¶
Inspect the snapshots of the table:
pyarrow.Table
committed_at: timestamp[ms] not null
snapshot_id: int64 not null
parent_id: int64
operation: string
manifest_list: string not null
summary: map<string, string>
child 0, entries: struct<key: string not null, value: string> not null
child 0, key: string not null
child 1, value: string
----
committed_at: [[2024-03-15 15:01:25.682,2024-03-15 15:01:25.730,2024-03-15 15:01:25.772]]
snapshot_id: [[805611270568163028,3679426539959220963,5588071473139865870]]
parent_id: [[null,805611270568163028,3679426539959220963]]
operation: [["append","overwrite","append"]]
manifest_list: [["s3://warehouse/default/table_metadata_snapshots/metadata/snap-805611270568163028-0-43637daf-ea4b-4ceb-b096-a60c25481eb5.avro","s3://warehouse/default/table_metadata_snapshots/metadata/snap-3679426539959220963-0-8be81019-adf1-4bb6-a127-e15217bd50b3.avro","s3://warehouse/default/table_metadata_snapshots/metadata/snap-5588071473139865870-0-1382dd7e-5fbc-4c51-9776-a832d7d0984e.avro"]]
summary: [[keys:["added-files-size","added-data-files","added-records","total-data-files","total-delete-files","total-records","total-files-size","total-position-deletes","total-equality-deletes"]values:["5459","1","3","1","0","3","5459","0","0"],keys:["added-files-size","added-data-files","added-records","total-data-files","total-records",...,"total-equality-deletes","total-files-size","deleted-data-files","deleted-records","removed-files-size"]values:["5459","1","3","1","3",...,"0","5459","1","3","5459"],keys:["added-files-size","added-data-files","added-records","total-data-files","total-delete-files","total-records","total-files-size","total-position-deletes","total-equality-deletes"]values:["5459","1","3","2","0","6","10918","0","0"]]]
Partitions¶
Inspect the partitions of the table:
pyarrow.Table
partition: struct<dt_month: int32, dt_day: date32[day]> not null
child 0, dt_month: int32
child 1, dt_day: date32[day]
spec_id: int32 not null
record_count: int64 not null
file_count: int32 not null
total_data_file_size_in_bytes: int64 not null
position_delete_record_count: int64 not null
position_delete_file_count: int32 not null
equality_delete_record_count: int64 not null
equality_delete_file_count: int32 not null
last_updated_at: timestamp[ms]
last_updated_snapshot_id: int64
----
partition: [
-- is_valid: all not null
-- child 0 type: int32
[null,null,612]
-- child 1 type: date32[day]
[null,2021-02-01,null]]
spec_id: [[2,1,0]]
record_count: [[1,1,2]]
file_count: [[1,1,2]]
total_data_file_size_in_bytes: [[641,641,1260]]
position_delete_record_count: [[0,0,0]]
position_delete_file_count: [[0,0,0]]
equality_delete_record_count: [[0,0,0]]
equality_delete_file_count: [[0,0,0]]
last_updated_at: [[2024-04-13 18:59:35.981,2024-04-13 18:59:35.465,2024-04-13 18:59:35.003]]
Entries¶
To show all the table's current manifest entries for both data and delete files.
pyarrow.Table
status: int8 not null
snapshot_id: int64 not null
sequence_number: int64 not null
file_sequence_number: int64 not null
data_file: struct<content: int8 not null, file_path: string not null, file_format: string not null, partition: struct<> not null, record_count: int64 not null, file_size_in_bytes: int64 not null, column_sizes: map<int32, int64>, value_counts: map<int32, int64>, null_value_counts: map<int32, int64>, nan_value_counts: map<int32, int64>, lower_bounds: map<int32, binary>, upper_bounds: map<int32, binary>, key_metadata: binary, split_offsets: list<item: int64>, equality_ids: list<item: int32>, sort_order_id: int32> not null
child 0, content: int8 not null
child 1, file_path: string not null
child 2, file_format: string not null
child 3, partition: struct<> not null
child 4, record_count: int64 not null
child 5, file_size_in_bytes: int64 not null
child 6, column_sizes: map<int32, int64>
child 0, entries: struct<key: int32 not null, value: int64> not null
child 0, key: int32 not null
child 1, value: int64
child 7, value_counts: map<int32, int64>
child 0, entries: struct<key: int32 not null, value: int64> not null
child 0, key: int32 not null
child 1, value: int64
child 8, null_value_counts: map<int32, int64>
child 0, entries: struct<key: int32 not null, value: int64> not null
child 0, key: int32 not null
child 1, value: int64
child 9, nan_value_counts: map<int32, int64>
child 0, entries: struct<key: int32 not null, value: int64> not null
child 0, key: int32 not null
child 1, value: int64
child 10, lower_bounds: map<int32, binary>
child 0, entries: struct<key: int32 not null, value: binary> not null
child 0, key: int32 not null
child 1, value: binary
child 11, upper_bounds: map<int32, binary>
child 0, entries: struct<key: int32 not null, value: binary> not null
child 0, key: int32 not null
child 1, value: binary
child 12, key_metadata: binary
child 13, split_offsets: list<item: int64>
child 0, item: int64
child 14, equality_ids: list<item: int32>
child 0, item: int32
child 15, sort_order_id: int32
readable_metrics: struct<city: struct<column_size: int64, value_count: int64, null_value_count: int64, nan_value_count: int64, lower_bound: string, upper_bound: string> not null, lat: struct<column_size: int64, value_count: int64, null_value_count: int64, nan_value_count: int64, lower_bound: double, upper_bound: double> not null, long: struct<column_size: int64, value_count: int64, null_value_count: int64, nan_value_count: int64, lower_bound: double, upper_bound: double> not null>
child 0, city: struct<column_size: int64, value_count: int64, null_value_count: int64, nan_value_count: int64, lower_bound: string, upper_bound: string> not null
child 0, column_size: int64
child 1, value_count: int64
child 2, null_value_count: int64
child 3, nan_value_count: int64
child 4, lower_bound: string
child 5, upper_bound: string
child 1, lat: struct<column_size: int64, value_count: int64, null_value_count: int64, nan_value_count: int64, lower_bound: double, upper_bound: double> not null
child 0, column_size: int64
child 1, value_count: int64
child 2, null_value_count: int64
child 3, nan_value_count: int64
child 4, lower_bound: double
child 5, upper_bound: double
child 2, long: struct<column_size: int64, value_count: int64, null_value_count: int64, nan_value_count: int64, lower_bound: double, upper_bound: double> not null
child 0, column_size: int64
child 1, value_count: int64
child 2, null_value_count: int64
child 3, nan_value_count: int64
child 4, lower_bound: double
child 5, upper_bound: double
----
status: [[1]]
snapshot_id: [[6245626162224016531]]
sequence_number: [[1]]
file_sequence_number: [[1]]
data_file: [
-- is_valid: all not null
-- child 0 type: int8
[0]
-- child 1 type: string
["s3://warehouse/default/cities/data/00000-0-80766b66-e558-4150-a5cf-85e4c609b9fe.parquet"]
-- child 2 type: string
["PARQUET"]
-- child 3 type: struct<>
-- is_valid: all not null
-- child 4 type: int64
[4]
-- child 5 type: int64
[1656]
-- child 6 type: map<int32, int64>
[keys:[1,2,3]values:[140,135,135]]
-- child 7 type: map<int32, int64>
[keys:[1,2,3]values:[4,4,4]]
-- child 8 type: map<int32, int64>
[keys:[1,2,3]values:[0,0,0]]
-- child 9 type: map<int32, int64>
[keys:[]values:[]]
-- child 10 type: map<int32, binary>
[keys:[1,2,3]values:[416D7374657264616D,8602B68311E34240,3A77BB5E9A9B5EC0]]
-- child 11 type: map<int32, binary>
[keys:[1,2,3]values:[53616E204672616E636973636F,F5BEF1B5678E4A40,304CA60A46651840]]
-- child 12 type: binary
[null]
-- child 13 type: list<item: int64>
[[4]]
-- child 14 type: list<item: int32>
[null]
-- child 15 type: int32
[null]]
readable_metrics: [
-- is_valid: all not null
-- child 0 type: struct<column_size: int64, value_count: int64, null_value_count: int64, nan_value_count: int64, lower_bound: string, upper_bound: string>
-- is_valid: all not null
-- child 0 type: int64
[140]
-- child 1 type: int64
[4]
-- child 2 type: int64
[0]
-- child 3 type: int64
[null]
-- child 4 type: string
["Amsterdam"]
-- child 5 type: string
["San Francisco"]
-- child 1 type: struct<column_size: int64, value_count: int64, null_value_count: int64, nan_value_count: int64, lower_bound: double, upper_bound: double>
-- is_valid: all not null
-- child 0 type: int64
[135]
-- child 1 type: int64
[4]
-- child 2 type: int64
[0]
-- child 3 type: int64
[null]
-- child 4 type: double
[37.773972]
-- child 5 type: double
[53.11254]
-- child 2 type: struct<column_size: int64, value_count: int64, null_value_count: int64, nan_value_count: int64, lower_bound: double, upper_bound: double>
-- is_valid: all not null
-- child 0 type: int64
[135]
-- child 1 type: int64
[4]
-- child 2 type: int64
[0]
-- child 3 type: int64
[null]
-- child 4 type: double
[-122.431297]
-- child 5 type: double
[6.0989]]
References¶
To show a table's known snapshot references:
pyarrow.Table
name: string not null
type: string not null
snapshot_id: int64 not null
max_reference_age_in_ms: int64
min_snapshots_to_keep: int32
max_snapshot_age_in_ms: int64
----
name: [["main","testTag"]]
type: [["BRANCH","TAG"]]
snapshot_id: [[2278002651076891950,2278002651076891950]]
max_reference_age_in_ms: [[null,604800000]]
min_snapshots_to_keep: [[null,10]]
max_snapshot_age_in_ms: [[null,604800000]]
Manifests¶
To show a table's current file manifests:
pyarrow.Table
content: int8 not null
path: string not null
length: int64 not null
partition_spec_id: int32 not null
added_snapshot_id: int64 not null
added_data_files_count: int32 not null
existing_data_files_count: int32 not null
deleted_data_files_count: int32 not null
added_delete_files_count: int32 not null
existing_delete_files_count: int32 not null
deleted_delete_files_count: int32 not null
partition_summaries: list<item: struct<contains_null: bool not null, contains_nan: bool, lower_bound: string, upper_bound: string>> not null
child 0, item: struct<contains_null: bool not null, contains_nan: bool, lower_bound: string, upper_bound: string>
child 0, contains_null: bool not null
child 1, contains_nan: bool
child 2, lower_bound: string
child 3, upper_bound: string
----
content: [[0]]
path: [["s3://warehouse/default/table_metadata_manifests/metadata/3bf5b4c6-a7a4-4b43-a6ce-ca2b4887945a-m0.avro"]]
length: [[6886]]
partition_spec_id: [[0]]
added_snapshot_id: [[3815834705531553721]]
added_data_files_count: [[1]]
existing_data_files_count: [[0]]
deleted_data_files_count: [[0]]
added_delete_files_count: [[0]]
existing_delete_files_count: [[0]]
deleted_delete_files_count: [[0]]
partition_summaries: [[ -- is_valid: all not null
-- child 0 type: bool
[false]
-- child 1 type: bool
[false]
-- child 2 type: string
["test"]
-- child 3 type: string
["test"]]]
Metadata Log Entries¶
To show table metadata log entries:
pyarrow.Table
timestamp: timestamp[ms] not null
file: string not null
latest_snapshot_id: int64
latest_schema_id: int32
latest_sequence_number: int64
----
timestamp: [[2024-04-28 17:03:00.214,2024-04-28 17:03:00.352,2024-04-28 17:03:00.445,2024-04-28 17:03:00.498]]
file: [["s3://warehouse/default/table_metadata_log_entries/metadata/00000-0b3b643b-0f3a-4787-83ad-601ba57b7319.metadata.json","s3://warehouse/default/table_metadata_log_entries/metadata/00001-f74e4b2c-0f89-4f55-822d-23d099fd7d54.metadata.json","s3://warehouse/default/table_metadata_log_entries/metadata/00002-97e31507-e4d9-4438-aff1-3c0c5304d271.metadata.json","s3://warehouse/default/table_metadata_log_entries/metadata/00003-6c8b7033-6ad8-4fe4-b64d-d70381aeaddc.metadata.json"]]
latest_snapshot_id: [[null,3958871664825505738,1289234307021405706,7640277914614648349]]
latest_schema_id: [[null,0,0,0]]
latest_sequence_number: [[null,0,0,0]]
History¶
To show a table's history:
pyarrow.Table
made_current_at: timestamp[ms] not null
snapshot_id: int64 not null
parent_id: int64
is_current_ancestor: bool not null
----
made_current_at: [[2024-06-18 16:17:48.768,2024-06-18 16:17:49.240,2024-06-18 16:17:49.343,2024-06-18 16:17:49.511]]
snapshot_id: [[4358109269873137077,3380769165026943338,4358109269873137077,3089420140651211776]]
parent_id: [[null,4358109269873137077,null,4358109269873137077]]
is_current_ancestor: [[true,false,true,true]]
Files¶
Inspect the data files in the current snapshot of the table:
pyarrow.Table
content: int8 not null
file_path: string not null
file_format: dictionary<values=string, indices=int32, ordered=0> not null
spec_id: int32 not null
record_count: int64 not null
file_size_in_bytes: int64 not null
column_sizes: map<int32, int64>
child 0, entries: struct<key: int32 not null, value: int64> not null
child 0, key: int32 not null
child 1, value: int64
value_counts: map<int32, int64>
child 0, entries: struct<key: int32 not null, value: int64> not null
child 0, key: int32 not null
child 1, value: int64
null_value_counts: map<int32, int64>
child 0, entries: struct<key: int32 not null, value: int64> not null
child 0, key: int32 not null
child 1, value: int64
nan_value_counts: map<int32, int64>
child 0, entries: struct<key: int32 not null, value: int64> not null
child 0, key: int32 not null
child 1, value: int64
lower_bounds: map<int32, binary>
child 0, entries: struct<key: int32 not null, value: binary> not null
child 0, key: int32 not null
child 1, value: binary
upper_bounds: map<int32, binary>
child 0, entries: struct<key: int32 not null, value: binary> not null
child 0, key: int32 not null
child 1, value: binary
key_metadata: binary
split_offsets: list<item: int64>
child 0, item: int64
equality_ids: list<item: int32>
child 0, item: int32
sort_order_id: int32
readable_metrics: struct<city: struct<column_size: int64, value_count: int64, null_value_count: int64, nan_value_count: int64, lower_bound: large_string, upper_bound: large_string> not null, lat: struct<column_size: int64, value_count: int64, null_value_count: int64, nan_value_count: int64, lower_bound: double, upper_bound: double> not null, long: struct<column_size: int64, value_count: int64, null_value_count: int64, nan_value_count: int64, lower_bound: double, upper_bound: double> not null>
child 0, city: struct<column_size: int64, value_count: int64, null_value_count: int64, nan_value_count: int64, lower_bound: string, upper_bound: string> not null
child 0, column_size: int64
child 1, value_count: int64
child 2, null_value_count: int64
child 3, nan_value_count: int64
child 4, lower_bound: large_string
child 5, upper_bound: large_string
child 1, lat: struct<column_size: int64, value_count: int64, null_value_count: int64, nan_value_count: int64, lower_bound: double, upper_bound: double> not null
child 0, column_size: int64
child 1, value_count: int64
child 2, null_value_count: int64
child 3, nan_value_count: int64
child 4, lower_bound: double
child 5, upper_bound: double
child 2, long: struct<column_size: int64, value_count: int64, null_value_count: int64, nan_value_count: int64, lower_bound: double, upper_bound: double> not null
child 0, column_size: int64
child 1, value_count: int64
child 2, null_value_count: int64
child 3, nan_value_count: int64
child 4, lower_bound: double
child 5, upper_bound: double
----
content: [[0,0]]
file_path: [["s3://warehouse/default/table_metadata_files/data/00000-0-9ea7d222-6457-467f-bad5-6fb125c9aa5f.parquet","s3://warehouse/default/table_metadata_files/data/00000-0-afa8893c-de71-4710-97c9-6b01590d0c44.parquet"]]
file_format: [["PARQUET","PARQUET"]]
spec_id: [[0,0]]
record_count: [[3,3]]
file_size_in_bytes: [[5459,5459]]
column_sizes: [[keys:[1,2,3,4,5,...,8,9,10,11,12]values:[49,78,128,94,118,...,118,118,94,78,109],keys:[1,2,3,4,5,...,8,9,10,11,12]values:[49,78,128,94,118,...,118,118,94,78,109]]]
value_counts: [[keys:[1,2,3,4,5,...,8,9,10,11,12]values:[3,3,3,3,3,...,3,3,3,3,3],keys:[1,2,3,4,5,...,8,9,10,11,12]values:[3,3,3,3,3,...,3,3,3,3,3]]]
null_value_counts: [[keys:[1,2,3,4,5,...,8,9,10,11,12]values:[1,1,1,1,1,...,1,1,1,1,1],keys:[1,2,3,4,5,...,8,9,10,11,12]values:[1,1,1,1,1,...,1,1,1,1,1]]]
nan_value_counts: [[keys:[]values:[],keys:[]values:[]]]
lower_bounds: [[keys:[1,2,3,4,5,...,8,9,10,11,12]values:[00,61,61616161616161616161616161616161,01000000,0100000000000000,...,009B6ACA38F10500,009B6ACA38F10500,9E4B0000,01,00000000000000000000000000000000],keys:[1,2,3,4,5,...,8,9,10,11,12]values:[00,61,61616161616161616161616161616161,01000000,0100000000000000,...,009B6ACA38F10500,009B6ACA38F10500,9E4B0000,01,00000000000000000000000000000000]]]
upper_bounds:[[keys:[1,2,3,4,5,...,8,9,10,11,12]values:[00,61,61616161616161616161616161616161,01000000,0100000000000000,...,009B6ACA38F10500,009B6ACA38F10500,9E4B0000,01,00000000000000000000000000000000],keys:[1,2,3,4,5,...,8,9,10,11,12]values:[00,61,61616161616161616161616161616161,01000000,0100000000000000,...,009B6ACA38F10500,009B6ACA38F10500,9E4B0000,01,00000000000000000000000000000000]]]
key_metadata: [[0100,0100]]
split_offsets:[[[],[]]]
equality_ids:[[[],[]]]
sort_order_id:[[[],[]]]
readable_metrics: [
-- is_valid: all not null
-- child 0 type: struct<column_size: int64, value_count: int64, null_value_count: int64, nan_value_count: int64, lower_bound: large_string, upper_bound: large_string>
-- is_valid: all not null
-- child 0 type: int64
[140]
-- child 1 type: int64
[4]
-- child 2 type: int64
[0]
-- child 3 type: int64
[null]
-- child 4 type: large_string
["Amsterdam"]
-- child 5 type: large_string
["San Francisco"]
-- child 1 type: struct<column_size: int64, value_count: int64, null_value_count: int64, nan_value_count: int64, lower_bound: double, upper_bound: double>
-- is_valid: all not null
-- child 0 type: int64
[135]
-- child 1 type: int64
[4]
-- child 2 type: int64
[0]
-- child 3 type: int64
[null]
-- child 4 type: double
[37.773972]
-- child 5 type: double
[53.11254]
-- child 2 type: struct<column_size: int64, value_count: int64, null_value_count: int64, nan_value_count: int64, lower_bound: double, upper_bound: double>
-- is_valid: all not null
-- child 0 type: int64
[135]
-- child 1 type: int64
[4]
-- child 2 type: int64
[0]
-- child 3 type: int64
[null]
-- child 4 type: double
[-122.431297]
-- child 5 type: double
[6.0989]]
Info
Content refers to type of content stored by the data file: 0 - Data, 1 - Position Deletes, 2 - Equality Deletes
To show only data files or delete files in the current snapshot, use table.inspect.data_files() and table.inspect.delete_files() respectively.
Add Files¶
Expert Iceberg users may choose to commit existing parquet files to the Iceberg table as data files, without rewriting them.
# Given that these parquet files have schema consistent with the Iceberg table
file_paths = [
"s3a://warehouse/default/existing-1.parquet",
"s3a://warehouse/default/existing-2.parquet",
]
# They can be added to the table without rewriting them
tbl.add_files(file_paths=file_paths)
# A new snapshot is committed to the table with manifests pointing to the existing parquet files
Name Mapping
Because add_files uses existing files without writing new parquet files that are aware of the Iceberg's schema, it requires the Iceberg's table to have a Name Mapping (The Name mapping maps the field names within the parquet files to the Iceberg field IDs). Hence, add_files requires that there are no field IDs in the parquet file's metadata, and creates a new Name Mapping based on the table's current schema if the table doesn't already have one.
Partitions
add_files only requires the client to read the existing parquet files' metadata footer to infer the partition value of each file. This implementation also supports adding files to Iceberg tables with partition transforms like MonthTransform, and TruncateTransform which preserve the order of the values after the transformation (Any Transform that has the preserves_order property set to True is supported). Please note that if the column statistics of the PartitionField's source column are not present in the parquet metadata, the partition value is inferred as None.
Maintenance Operations
Because add_files commits the existing parquet files to the Iceberg Table as any other data file, destructive maintenance operations like expiring snapshots will remove them.
Schema evolution¶
PyIceberg supports full schema evolution through the Python API. It takes care of setting the field-IDs and makes sure that only non-breaking changes are done (can be overriden).
In the examples below, the .update_schema() is called from the table itself.
You can also initiate a transaction if you want to make more changes than just evolving the schema:
with table.transaction() as transaction:
with transaction.update_schema() as update_schema:
update.add_column("some_other_field", IntegerType(), "doc")
# ... Update properties etc
Union by Name¶
Using .union_by_name() you can merge another schema into an existing schema without having to worry about field-IDs:
from pyiceberg.catalog import load_catalog
from pyiceberg.schema import Schema
from pyiceberg.types import NestedField, StringType, DoubleType, LongType
catalog = load_catalog()
schema = Schema(
NestedField(1, "city", StringType(), required=False),
NestedField(2, "lat", DoubleType(), required=False),
NestedField(3, "long", DoubleType(), required=False),
)
table = catalog.create_table("default.locations", schema)
new_schema = Schema(
NestedField(1, "city", StringType(), required=False),
NestedField(2, "lat", DoubleType(), required=False),
NestedField(3, "long", DoubleType(), required=False),
NestedField(10, "population", LongType(), required=False),
)
with table.update_schema() as update:
update.union_by_name(new_schema)
Now the table has the union of the two schemas print(table.schema()):
table {
1: city: optional string
2: lat: optional double
3: long: optional double
4: population: optional long
}
Add column¶
Using add_column you can add a column, without having to worry about the field-id:
with table.update_schema() as update:
update.add_column("retries", IntegerType(), "Number of retries to place the bid")
# In a struct
update.add_column("details.confirmed_by", StringType(), "Name of the exchange")
Rename column¶
Renaming a field in an Iceberg table is simple:
with table.update_schema() as update:
update.rename_column("retries", "num_retries")
# This will rename `confirmed_by` to `exchange`
update.rename_column("properties.confirmed_by", "exchange")
Move column¶
Move a field inside of struct:
with table.update_schema() as update:
update.move_first("symbol")
update.move_after("bid", "ask")
# This will move `confirmed_by` before `exchange`
update.move_before("details.created_by", "details.exchange")
Update column¶
Update a fields' type, description or required.
with table.update_schema() as update:
# Promote a float to a double
update.update_column("bid", field_type=DoubleType())
# Make a field optional
update.update_column("symbol", required=False)
# Update the documentation
update.update_column("symbol", doc="Name of the share on the exchange")
Be careful, some operations are not compatible, but can still be done at your own risk by setting allow_incompatible_changes:
with table.update_schema(allow_incompatible_changes=True) as update:
# Incompatible change, cannot require an optional field
update.update_column("symbol", required=True)
Delete column¶
Delete a field, careful this is a incompatible change (readers/writers might expect this field):
with table.update_schema(allow_incompatible_changes=True) as update:
update.delete_column("some_field")
Partition evolution¶
PyIceberg supports partition evolution. See the partition evolution for more details.
The API to use when evolving partitions is the update_spec API on the table.
with table.update_spec() as update:
update.add_field("id", BucketTransform(16), "bucketed_id")
update.add_field("event_ts", DayTransform(), "day_ts")
Updating the partition spec can also be done as part of a transaction with other operations.
with table.transaction() as transaction:
with transaction.update_spec() as update_spec:
update_spec.add_field("id", BucketTransform(16), "bucketed_id")
update_spec.add_field("event_ts", DayTransform(), "day_ts")
# ... Update properties etc
Add fields¶
New partition fields can be added via the add_field API which takes in the field name to partition on,
the partition transform, and an optional partition name. If the partition name is not specified,
one will be created.
with table.update_spec() as update:
update.add_field("id", BucketTransform(16), "bucketed_id")
update.add_field("event_ts", DayTransform(), "day_ts")
# identity is a shortcut API for adding an IdentityTransform
update.identity("some_field")
Remove fields¶
Partition fields can also be removed via the remove_field API if it no longer makes sense to partition on those fields.
with table.update_spec() as update:some_partition_name
# Remove the partition field with the name
update.remove_field("some_partition_name")
Rename fields¶
Partition fields can also be renamed via the rename_field API.
with table.update_spec() as update:
# Rename the partition field with the name bucketed_id to sharded_id
update.rename_field("bucketed_id", "sharded_id")
Table properties¶
Set and remove properties through the Transaction API:
with table.transaction() as transaction:
transaction.set_properties(abc="def")
assert table.properties == {"abc": "def"}
with table.transaction() as transaction:
transaction.remove_properties("abc")
assert table.properties == {}
Or, without context manager:
table = table.transaction().set_properties(abc="def").commit_transaction()
assert table.properties == {"abc": "def"}
table = table.transaction().remove_properties("abc").commit_transaction()
assert table.properties == {}
Snapshot properties¶
Optionally, Snapshot properties can be set while writing to a table using append or overwrite API:
tbl.append(df, snapshot_properties={"abc": "def"})
# or
tbl.overwrite(df, snapshot_properties={"abc": "def"})
assert tbl.metadata.snapshots[-1].summary["abc"] == "def"
Snapshot Management¶
Manage snapshots with operations through the Table API:
# To run a specific operation
table.manage_snapshots().create_tag(snapshot_id, "tag123").commit()
# To run multiple operations
table.manage_snapshots()
.create_tag(snapshot_id1, "tag123")
.create_tag(snapshot_id2, "tag456")
.commit()
# Operations are applied on commit.
You can also use context managers to make more changes:
with table.manage_snapshots() as ms:
ms.create_branch(snapshot_id1, "Branch_A").create_tag(snapshot_id2, "tag789")
Query the data¶
To query a table, a table scan is needed. A table scan accepts a filter, columns, optionally a limit and a snapshot ID:
from pyiceberg.catalog import load_catalog
from pyiceberg.expressions import GreaterThanOrEqual
catalog = load_catalog("default")
table = catalog.load_table("nyc.taxis")
scan = table.scan(
row_filter=GreaterThanOrEqual("trip_distance", 10.0),
selected_fields=("VendorID", "tpep_pickup_datetime", "tpep_dropoff_datetime"),
limit=100,
)
# Or filter using a string predicate
scan = table.scan(
row_filter="trip_distance > 10.0",
)
[task.file.file_path for task in scan.plan_files()]
The low level API plan_files methods returns a set of tasks that provide the files that might contain matching rows:
In this case it is up to the engine itself to filter the file itself. Below, to_arrow() and to_duckdb() that already do this for you.
Apache Arrow¶
Requirements
This requires pyarrow to be installed.
Using PyIceberg it is filter out data from a huge table and pull it into a PyArrow table:
table.scan(
row_filter=GreaterThanOrEqual("trip_distance", 10.0),
selected_fields=("VendorID", "tpep_pickup_datetime", "tpep_dropoff_datetime"),
).to_arrow()
This will return a PyArrow table:
pyarrow.Table
VendorID: int64
tpep_pickup_datetime: timestamp[us, tz=+00:00]
tpep_dropoff_datetime: timestamp[us, tz=+00:00]
----
VendorID: [[2,1,2,1,1,...,2,2,2,2,2],[2,1,1,1,2,...,1,1,2,1,2],...,[2,2,2,2,2,...,2,6,6,2,2],[2,2,2,2,2,...,2,2,2,2,2]]
tpep_pickup_datetime: [[2021-04-01 00:28:05.000000,...,2021-04-30 23:44:25.000000]]
tpep_dropoff_datetime: [[2021-04-01 00:47:59.000000,...,2021-05-01 00:14:47.000000]]
This will only pull in the files that that might contain matching rows.
One can also return a PyArrow RecordBatchReader, if reading one record batch at a time is preferred:
table.scan(
row_filter=GreaterThanOrEqual("trip_distance", 10.0),
selected_fields=("VendorID", "tpep_pickup_datetime", "tpep_dropoff_datetime"),
).to_arrow_batch_reader()
Pandas¶
Requirements
This requires pandas to be installed.
PyIceberg makes it easy to filter out data from a huge table and pull it into a Pandas dataframe locally. This will only fetch the relevant Parquet files for the query and apply the filter. This will reduce IO and therefore improve performance and reduce cost.
table.scan(
row_filter="trip_distance >= 10.0",
selected_fields=("VendorID", "tpep_pickup_datetime", "tpep_dropoff_datetime"),
).to_pandas()
This will return a Pandas dataframe:
VendorID tpep_pickup_datetime tpep_dropoff_datetime
0 2 2021-04-01 00:28:05+00:00 2021-04-01 00:47:59+00:00
1 1 2021-04-01 00:39:01+00:00 2021-04-01 00:57:39+00:00
2 2 2021-04-01 00:14:42+00:00 2021-04-01 00:42:59+00:00
3 1 2021-04-01 00:17:17+00:00 2021-04-01 00:43:38+00:00
4 1 2021-04-01 00:24:04+00:00 2021-04-01 00:56:20+00:00
... ... ... ...
116976 2 2021-04-30 23:56:18+00:00 2021-05-01 00:29:13+00:00
116977 2 2021-04-30 23:07:41+00:00 2021-04-30 23:37:18+00:00
116978 2 2021-04-30 23:38:28+00:00 2021-05-01 00:12:04+00:00
116979 2 2021-04-30 23:33:00+00:00 2021-04-30 23:59:00+00:00
116980 2 2021-04-30 23:44:25+00:00 2021-05-01 00:14:47+00:00
[116981 rows x 3 columns]
It is recommended to use Pandas 2 or later, because it stores the data in an Apache Arrow backend which avoids copies of data.
DuckDB¶
Requirements
This requires DuckDB to be installed.
A table scan can also be converted into a in-memory DuckDB table:
con = table.scan(
row_filter=GreaterThanOrEqual("trip_distance", 10.0),
selected_fields=("VendorID", "tpep_pickup_datetime", "tpep_dropoff_datetime"),
).to_duckdb(table_name="distant_taxi_trips")
Using the cursor that we can run queries on the DuckDB table:
print(
con.execute(
"SELECT tpep_dropoff_datetime - tpep_pickup_datetime AS duration FROM distant_taxi_trips LIMIT 4"
).fetchall()
)
[
(datetime.timedelta(seconds=1194),),
(datetime.timedelta(seconds=1118),),
(datetime.timedelta(seconds=1697),),
(datetime.timedelta(seconds=1581),),
]
Ray¶
Requirements
This requires Ray to be installed.
A table scan can also be converted into a Ray dataset:
ray_dataset = table.scan(
row_filter=GreaterThanOrEqual("trip_distance", 10.0),
selected_fields=("VendorID", "tpep_pickup_datetime", "tpep_dropoff_datetime"),
).to_ray()
This will return a Ray dataset:
Dataset(
num_blocks=1,
num_rows=1168798,
schema={
VendorID: int64,
tpep_pickup_datetime: timestamp[us, tz=UTC],
tpep_dropoff_datetime: timestamp[us, tz=UTC]
}
)
Using Ray Dataset API to interact with the dataset:
print(ray_dataset.take(2))
[
{
"VendorID": 2,
"tpep_pickup_datetime": datetime.datetime(2008, 12, 31, 23, 23, 50),
"tpep_dropoff_datetime": datetime.datetime(2009, 1, 1, 0, 34, 31),
},
{
"VendorID": 2,
"tpep_pickup_datetime": datetime.datetime(2008, 12, 31, 23, 5, 3),
"tpep_dropoff_datetime": datetime.datetime(2009, 1, 1, 16, 10, 18),
},
]
Daft¶
PyIceberg interfaces closely with Daft Dataframes (see also: Daft integration with Iceberg) which provides a full lazily optimized query engine interface on top of PyIceberg tables.
Requirements
This requires Daft to be installed.
A table can be read easily into a Daft Dataframe:
df = table.to_daft() # equivalent to `daft.read_iceberg(table)`
df = df.where(df["trip_distance"] >= 10.0)
df = df.select("VendorID", "tpep_pickup_datetime", "tpep_dropoff_datetime")
This returns a Daft Dataframe which is lazily materialized. Printing df will display the schema:
╭──────────┬───────────────────────────────┬───────────────────────────────╮
│ VendorID ┆ tpep_pickup_datetime ┆ tpep_dropoff_datetime │
│ --- ┆ --- ┆ --- │
│ Int64 ┆ Timestamp(Microseconds, None) ┆ Timestamp(Microseconds, None) │
╰──────────┴───────────────────────────────┴───────────────────────────────╯
(No data to display: Dataframe not materialized)
We can execute the Dataframe to preview the first few rows of the query with df.show().
This is correctly optimized to take advantage of Iceberg features such as hidden partitioning and file-level statistics for efficient reads.
╭──────────┬───────────────────────────────┬───────────────────────────────╮
│ VendorID ┆ tpep_pickup_datetime ┆ tpep_dropoff_datetime │
│ --- ┆ --- ┆ --- │
│ Int64 ┆ Timestamp(Microseconds, None) ┆ Timestamp(Microseconds, None) │
╞══════════╪═══════════════════════════════╪═══════════════════════════════╡
│ 2 ┆ 2008-12-31T23:23:50.000000 ┆ 2009-01-01T00:34:31.000000 │
├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2 ┆ 2008-12-31T23:05:03.000000 ┆ 2009-01-01T16:10:18.000000 │
╰──────────┴───────────────────────────────┴───────────────────────────────╯
(Showing first 2 rows)