Overview

If a table does not have a primary key defined, it is an append table. Compared to the primary key table, it does not have the ability to directly receive changelogs. It cannot be directly updated with data through upsert. It can only receive incoming data from append data.

CREATE TABLE my_table (
    product_id BIGINT,
    price DOUBLE,
    sales BIGINT
) WITH (
    -- 'target-file-size' = '256 MB',
    -- 'file.format' = 'parquet',
    -- 'file.compression' = 'zstd',
    -- 'file.compression.zstd-level' = '3'
);

Batch write and batch read in typical application scenarios, similar to a regular Hive partition table, but compared to the Hive table, it can bring:

Time travel enables reproducible queries that use exactly the same table snapshot, or lets users easily examine changes. Version rollback allows users to quickly correct problems by resetting tables to a good state.
Scan planning is fast — data files are pruned with partition and column-level stats, using table metadata. File Index (BloomFilter, Bitmap, Range Bitmap) and aggregate push-down further accelerate queries.
Schema evolution supports add, drop, update, or rename columns, and has no side-effects.
Rich ecosystem — adds tables to compute engines including Flink, Spark, Hive, Trino, Presto, StarRocks, and Doris, working just like a SQL table.
Incremental Clustering with z-order/hilbert/order sorting to optimize data layout at low cost.
Streaming read & write like a queue, DELETE / UPDATE / MERGE INTO support low-cost row-level operations.

title: “Streaming” weight: 2 type: docs aliases:

/append-table/streaming.html

Append Streaming

You can stream write to the Append table in a very flexible way through Flink, or read the Append table through Flink, using it like a queue. The only difference is that its latency is in minutes. Its advantages are very low cost and the ability to push down filters and projection.

Pre small files merging

“Pre” means that this compact occurs before committing files to the snapshot.

If Flink's checkpoint interval is short (for example, 30 seconds), each snapshot may produce lots of small changelog files. Too many files may put a burden on the distributed storage cluster.

In order to compact small changelog files into large ones, you can set the table option precommit-compact = true. Default value of this option is false, if true, it will add a compact coordinator and worker operator after the writer operator, which copies changelog files into large ones.

Post small files merging

“Post” means that this compact occurs after committing files to the snapshot.

In streaming write job, without bucket definition, there is no compaction in writer, instead, will use Compact Coordinator to scan the small files and pass compaction task to Compact Worker. In streaming mode, if you run insert sql in flink, the topology will be like this:

Do not worry about backpressure, compaction never backpressure.

If you set write-only to true, the Compact Coordinator and Compact Worker will be removed in the topology.

The auto compaction is only supported in Flink engine streaming mode. You can also start a compaction job in Flink by Flink action in Paimon and disable all the other compactions by setting write-only.

Streaming Query

You can stream the Append table and use it like a Message Queue. As with primary key tables, there are two options for streaming reads:

By default, Streaming read produces the latest snapshot on the table upon first startup, and continue to read the latest incremental records.
You can specify scan.mode, scan.snapshot-id, scan.timestamp-millis and/or scan.file-creation-time-millis to stream read incremental only.

Similar to flink-kafka, order is not guaranteed by default, if your data has some sort of order requirement, you also need to consider defining a bucket-key, see [Bucketed Append]({{< ref “append-table/bucketed” >}})

Aggregate push down

Append Table supports aggregate push down:

SELECT COUNT(*) FROM TABLE WHERE DT = '20230101';

This query can be accelerated during compilation and returns very quickly.

For Spark SQL, table with default metadata.stats-mode can be accelerated:

SELECT MIN(a), MAX(b) FROM TABLE WHERE DT = '20230101';

SELECT * FROM TABLE ORDER BY a LIMIT 1;

Min max topN query can be also accelerated during compilation and returns very quickly.

Data Skipping By Order

Paimon by default records the maximum and minimum values of each field in the manifest file.

In the query, according to the WHERE condition of the query, together with the statistics in the manifest we can perform file filtering. If the filtering effect is good, the query that would have cost minutes will be accelerated to milliseconds to complete the execution.

Often the data distribution is not always ideal for filtering, so can we sort the data by the field in WHERE condition? You can take a look at [Flink COMPACT Action]({{< ref “maintenance/dedicated-compaction#sort-compact” >}}), [Flink COMPACT Procedure]({{< ref “flink/procedures” >}}) or [Spark COMPACT Procedure]({{< ref “spark/procedures” >}}).

Data Skipping By File Index

You can use file index too, it filters files by indexing on the reading side.

Define file-index.bitmap.columns, Data file index is an external index file and Paimon will create its corresponding index file for each file. If the index file is too small, it will be stored directly in the manifest, otherwise in the directory of the data file. Each data file corresponds to an index file, which has a separate file definition and can contain different types of indexes with multiple columns.

Different file indexes may be efficient in different scenarios. For example bloom filter may speed up query in point lookup scenario. Using a bitmap may consume more space but can result in greater accuracy.

[BloomFilter]({{< ref “concepts/spec/fileindex#index-bloomfilter” >}}): file-index.bloom-filter.columns.
[Bitmap]({{< ref “concepts/spec/fileindex#index-bitmap” >}}): file-index.bitmap.columns.
[Range Bitmap]({{< ref “concepts/spec/fileindex#index-range-bitmap” >}}): file-index.range-bitmap.columns.

If you want to add file index to existing table, without any rewrite, you can use rewrite_file_index procedure. Before we use the procedure, you should config appropriate configurations in target table. You can use ALTER clause to config file-index.<filter-type>.columns to the table.

How to invoke: see [flink procedures]({{< ref “flink/procedures#procedures” >}})

Row Level Operations

Now, only Spark SQL supports DELETE & UPDATE & MERGE INTO, you can take a look at [Spark Write]({{< ref “spark/sql-write” >}}).

Example:

DELETE FROM my_table WHERE currency = 'UNKNOWN';

Update append table has two modes:

COW (Copy on Write): search for the hit files and then rewrite each file to remove the data that needs to be deleted from the files. This operation is costly.
MOW (Merge on Write): By specifying 'deletion-vectors.enabled' = 'true', the Deletion Vectors mode can be enabled. Only marks certain records of the corresponding file for deletion and writes the deletion file, without rewriting the entire file.