This is an automated email from the ASF dual-hosted git repository.
lzljs3620320 pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/paimon-website.git
The following commit(s) were added to refs/heads/master by this push:
new 986ded5ed feat: remove legacy pypaimon release
986ded5ed is described below
commit 986ded5ed751a54124d3008a8a3d4616c93d6387
Author: JingsongLi <[email protected]>
AuthorDate: Tue Dec 9 10:47:15 2025 +0800
feat: remove legacy pypaimon release
---
community/docs/releases/release-pypaimon-0.2.0.md | 178 ----------------------
1 file changed, 178 deletions(-)
diff --git a/community/docs/releases/release-pypaimon-0.2.0.md
b/community/docs/releases/release-pypaimon-0.2.0.md
deleted file mode 100644
index ebf56e263..000000000
--- a/community/docs/releases/release-pypaimon-0.2.0.md
+++ /dev/null
@@ -1,178 +0,0 @@
----
-title: "PyPaimon Release 0.2.0"
-type: release
-version: pypaimon-0.2.0
-weight: 91
----
-
-# PyPaimon 0.2.0 Available
-
-Dec 19, 2024 - Zelin Yu ([email protected])
-
-The Apache Paimon PMC officially announces the release of PyPaimon 0.2.0.
Because we didn't release 0.1.0,
-this is the first version.
-
-## What is PyPaimon?
-
-[PyPaimon](https://github.com/apache/paimon-python) is the Python SDK of
Apache Paimon. It provides a way
-for users to get data from Paimon tables with Python for data analysis, and
write data back to Paimon tables.
-
-## Version Overview
-
-The first version of PyPaimon supports following features:
-
-1. Connect to `Catalog`.
-2. Get or create table.
-3. Batch read: Filter and projection pushdown, and parallelly reading data as
Apache Arrow, Pandas, DuckDB and Ray format.
-4. Batch write: Insert into or overwrite table with Apache Arrow and Pandas
data.
-
-The detailed document can found at
https://paimon.apache.org/docs/master/program-api/python-api/.
-
-### Connect to Catalog
-
-You can create a `Catalog` with options just like in SQL:
-
-```python
-from pypaimon.py4j import Catalog
-
-catalog_options = {
- 'warehouse': 'path/to/warehouse',
- 'metastore': 'filesystem'
- # other options
-}
-
-catalog = Catalog.create(catalog_options)
-```
-
-You can connect to any `Catalog` supported by Java. PyPaimon has built-in
support for `filesystem`, `Jdbc` and `hive` catalog.
-If you want to connect to your self-defined catalogs, you can add the
dependency jars in following way:
-
-```python
-import os
-from pypaimon.py4j import constants
-
-os.environ[constants.PYPAIMON_JAVA_CLASSPATH] = '/path/to/jars/*'
-```
-
-### Get or create table
-
-You can get a existed table from `Catalog` by its identifier:
-
-```python
-table = catalog.get_table('database_name.table_name')
-```
-
-You can also create a new table. The table field definitions are described by
`pyarrow.Schema`, and you can set primary keys,
-partition keys, table options and comment.
-
-```python
-import pyarrow as pa
-from pypaimon import Schema
-
-# field definitions
-pa_schema = pa.schema([
- ('dt', pa.string()),
- ('hh', pa.string()),
- ('pk', pa.int64()),
- ('value', pa.string())
-])
-# table schema
-schema = Schema(
- pa_schema=pa_schema,
- partition_keys=['dt', 'hh'],
- primary_keys=['dt', 'hh', 'pk'],
- options={'bucket': '2'},
- comment='my test table'
-)
-
-# create table
-catalog.create_table(identifier='default.test_table', schema=schema,
ignore_if_exists=False)
-```
-
-Then you can get table read and write interfaces from table.
-
-## Batch read
-
-Assume that you already hava the table `default.test_table` described in the
previous section. Let's see how to read data from it.
-
-```python
-from pypaimon.py4j import Catalog
-
-# set 'max-workers' (thread numbers) for parallelly reading
-catalog_options = {
- 'warehouse': 'path/to/warehouse',
- 'metastore': 'filesystem',
- 'max-workers': '4'
-}
-catalog = Catalog.create(catalog_options)
-table = catalog.get_table('default.test_table')
-
-# use ReadBuilder to perform filter and projection pushdown
-read_builder = table.new_read_builder()
-
-# select partition: dt='2024-12-01',hh='12'
-predicate_builder = read_builder.new_predicate_builder()
-dt_predicate = predicate_builder.equal('dt', '2024-12-01')
-dt_hh = predicate_builder.equal('hh', '12')
-partition_predicate = predicate_builder.and_([dt_predicate, dt_hh])
-read_builder = read_builder.with_filter(partition_predicate)
-
-# select pk and value
-read_builder = read_builder.with_projection(['pk', 'value'])
-
-# plan splits
-table_scan = read_builder.new_scan()
-splits = table_scan.splits()
-
-# read data to pandas.DataFrame
-df = table_read.to_pandas(splits)
-```
-
-Then you can do some analysis on the dataframe with Python.
-
-## Batch Write
-
-Assume that you already hava the table `default.test_table` described in the
previous section. Let's see how to write or overwrite it.
-
-First, assume that you have a dataframe data of 2024-12-02, 12 o'clock, and
you want to write it into the table.
-
-```python
-write_builder = table.new_batch_write_builder()
-table_write = write_builder.new_write()
-table_commit = write_builder.new_commit()
-
-# you can write data many times before committing
-dataframe = ...
-table_write.write_pandas(dataframe)
-
-commit_messages = table_write.prepare_commit()
-table_commit.commit(commit_messages)
-
-table_write.close()
-table_commit.close()
-```
-
-Let's see how to overwrite the partition 'dt=2024-12-02,hh=12' with new data.
-```python
-write_builder = table.new_batch_write_builder()
-# set partition to overwrite
-write_builder = write_builder.overwrite({'dt': '2024-01-01', 'hh': '12'})
-
-table_write = write_builder.new_write()
-table_commit = write_builder.new_commit()
-
-# then write data
-dataframe = ...
-table_write.write_pandas(dataframe)
-
-commit_messages = table_write.prepare_commit()
-table_commit.commit(commit_messages)
-
-table_write.close()
-table_commit.close()
-```
-
-### Various data formats
-
-PyPaimon supports reading data in following formats: Pandas, Apache Arrow and
DuckDB, and writing data in following
-formats: Pandas, Apache Arrow. Please refer to the
[document](https://paimon.apache.org/docs/master/program-api/python-api/) for
details.
