Aggregation
This documentation is for an unreleased version of Apache Paimon. We recommend you use the latest stable version.

Aggregation #

NOTE: Always set table.exec.sink.upsert-materialize to NONE in Flink SQL TableConfig.

Sometimes users only care about aggregated results. The aggregation merge engine aggregates each value field with the latest data one by one under the same primary key according to the aggregate function.

Each field not part of the primary keys can be given an aggregate function, specified by the fields.<field-name>.aggregate-function table property, otherwise it will use last_non_null_value aggregation as default. For example, consider the following table definition.

CREATE TABLE my_table (
    product_id BIGINT,
    price DOUBLE,
    sales BIGINT,
    PRIMARY KEY (product_id) NOT ENFORCED
) WITH (
    'merge-engine' = 'aggregation',
    'fields.price.aggregate-function' = 'max',
    'fields.sales.aggregate-function' = 'sum'
);

Field price will be aggregated by the max function, and field sales will be aggregated by the sum function. Given two input records <1, 23.0, 15> and <1, 30.2, 20>, the final result will be <1, 30.2, 35>.

Aggregation Functions #

Current supported aggregate functions and data types are:

sum #

The sum function aggregates the values across multiple rows. It supports DECIMAL, TINYINT, SMALLINT, INTEGER, BIGINT, FLOAT, and DOUBLE data types.

product #

The product function can compute product values across multiple lines. It supports DECIMAL, TINYINT, SMALLINT, INTEGER, BIGINT, FLOAT, and DOUBLE data types.

count #

In scenarios where counting rows that match a specific condition is required, you can use the SUM function to achieve this. By expressing a condition as a Boolean value (TRUE or FALSE) and converting it into a numerical value, you can effectively count the rows. In this approach, TRUE is converted to 1, and FALSE is converted to 0.

For example, if you have a table orders and want to count the number of rows that meet a specific condition, you can use the following query:

SELECT SUM(CASE WHEN condition THEN 1 ELSE 0 END) AS count
FROM orders;

max #

The max function identifies and retains the maximum value. It supports CHAR, VARCHAR, DECIMAL, TINYINT, SMALLINT, INTEGER, BIGINT, FLOAT, DOUBLE, DATE, TIME, TIMESTAMP, and TIMESTAMP_LTZ data types.

min #

The min function identifies and retains the minimum value. It supports CHAR, VARCHAR, DECIMAL, TINYINT, SMALLINT, INTEGER, BIGINT, FLOAT, DOUBLE, DATE, TIME, TIMESTAMP, and TIMESTAMP_LTZ data types.

last_value #

The last_value function replaces the previous value with the most recently imported value. It supports all data types.

last_non_null_value #

The last_non_null_value function replaces the previous value with the latest non-null value. It supports all data types.

listagg #

The listagg function concatenates multiple string values into a single string. It supports STRING data type. Each field not part of the primary keys can be given a list agg delimiter, specified by the fields..list-agg-delimiter table property, otherwise it will use “,” as default.

bool_and #

The bool_and function evaluates whether all values in a boolean set are true. It supports BOOLEAN data type.

bool_or #

The bool_or function checks if at least one value in a boolean set is true. It supports BOOLEAN data type.

first_value #

The first_value function retrieves the first null value from a data set. It supports all data types.

first_non_null_value #

The first_non_null_value function selects the first non-null value in a data set. It supports all data types.

rbm32 #

The rbm32 function aggregates multiple serialized 32-bit RoaringBitmap into a single RoaringBitmap. It supports VARBINARY data type which must be serialized 32-bit RoaringBitmap.

RoaringBitmap is a compressed bitmap that efficiently represents sets of integers. The rbm32 aggregator is useful for scenarios where you need to merge multiple bitmap sets or use case requiring set union operations on large integer datasets.

Example:

-- Create a table to store user visit data with bitmap aggregation
CREATE TABLE user_visits (
  user_id INT PRIMARY KEY NOT ENFORCED,
  visit_bitmap VARBINARY
) WITH (
  'merge-engine' = 'aggregation',
  'fields.visit_bitmap.aggregate-function' = 'rbm32'
);

-- Register a UDF to create RoaringBitmap from user IDs
-- CREATE TEMPORARY FUNCTION TO_BITMAP AS 'BitmapUDF';

-- Insert data with bitmap representations
INSERT INTO user_visits VALUES
  (1, TO_BITMAP(100, 101, 102)),  -- User 1 visited pages 100, 101, 102
  (2, TO_BITMAP(101, 103)),       -- User 2 visited pages 101, 103
  (3, TO_BITMAP(102, 104));       -- User 3 visited pages 102, 104

-- When the same user_id is inserted again, the bitmaps will be merged
INSERT INTO user_visits VALUES
  (1, TO_BITMAP(103, 105)),       -- User 1 also visited pages 103, 105
  (2, TO_BITMAP(104, 106));       -- User 2 also visited pages 104, 106

-- The final result will have merged bitmaps for each user
-- User 1: pages 100, 101, 102, 103, 105
-- User 2: pages 101, 103, 104, 106
-- User 3: pages 102, 104

rbm64 #

The rbm64 function aggregates multiple serialized 64-bit Roaring64Bitmap into a single Roaring64Bitmap. It supports VARBINARY data type which must be serialized 64-bit RoaringBitmap.

Similar to rbm32, but supports 64-bit integers, making it suitable for scenarios with very large integer values or when you need to represent sets with values beyond the 32-bit range (up to 2^31-1).

Example:

-- Create a table to store large-scale user interaction data
CREATE TABLE user_interactions (
  session_id BIGINT PRIMARY KEY NOT ENFORCED,
  interaction_bitmap VARBINARY
) WITH (
  'merge-engine' = 'aggregation',
  'fields.interaction_bitmap.aggregate-function' = 'rbm64'
);

-- Register a UDF to create Roaring64Bitmap from large user IDs
-- CREATE TEMPORARY FUNCTION TO_BITMAP64 AS 'Bitmap64UDF';

-- Insert data with 64-bit bitmap representations
INSERT INTO user_interactions VALUES
  (1001, TO_BITMAP64(1000000001L, 1000000002L, 1000000003L)),
  (1002, TO_BITMAP64(1000000002L, 1000000004L)),
  (1003, TO_BITMAP64(1000000003L, 1000000005L));

-- Merge additional interactions
INSERT INTO user_interactions VALUES
  (1001, TO_BITMAP64(1000000004L, 1000000006L)),
  (1002, TO_BITMAP64(1000000005L, 1000000007L));

-- The final result will have merged 64-bit bitmaps for each session

The rbm32 and rbm64 aggregators work by:

  1. Deserializing the input VARBINARY data into RoaringBitmap objects
  2. Performing bitwise OR operations to merge the bitmaps
  3. Serializing the result back to VARBINARY format

Working with RoaringBitmap Functions:

Paimon currently does not provide built-in Flink UDFs for bitmap creation. You have two options:

  1. Create bitmaps programmatically: in your application code and insert serialized bytes
  2. Create custom Flink UDFs: to convert raw integers to serialized bitmap format

Here are examples of both approaches:

Option 1: Programmatic Bitmap Creation (Java/Scala)

// Create bitmaps programmatically
RoaringBitmap32 bitmap1 = RoaringBitmap32.bitmapOf(100, 101, 102);
RoaringBitmap32 bitmap2 = RoaringBitmap32.bitmapOf(101, 103);
RoaringBitmap32 bitmap3 = RoaringBitmap32.bitmapOf(102, 104);

byte[] serialized1 = bitmap1.serialize();
byte[] serialized2 = bitmap2.serialize();
byte[] serialized3 = bitmap3.serialize();

INSERT INTO user_page_visits VALUES
    (1, CAST(x'...' AS VARBINARY)),  -- serialized1 bytes
    (2, CAST(x'...' AS VARBINARY)),  -- serialized2 bytes
    (3, CAST(x'...' AS VARBINARY));  -- serialized3 bytes

Option 2: Custom Flink UDFs

-- Create the aggregation table
CREATE TABLE user_page_visits (
    user_id INT PRIMARY KEY NOT ENFORCED,
    page_visits VARBINARY
) WITH (
    'merge-engine' = 'aggregation',
    'fields.page_visits.aggregate-function' = 'rbm32'
);

-- Register custom UDFs (you need to implement these)

-- Use the UDFs
INSERT INTO user_page_visits VALUES
    (1, TO_BITMAP(100, 101, 102)),
    (2, TO_BITMAP(101, 103)),
    (3, TO_BITMAP(102, 104));

-- Query with UDFs
SELECT user_id, FROM_BITMAP(page_visits) as unique_pages
FROM user_page_visits;

SELECT user_id,
       BITMAP_CONTAINS(page_visits, 101) as visited_page_101
FROM user_page_visits
WHERE user_id = 1;

Sample UDF Implementation (Java):

// BitmapUDF - Converts integers to serialized RoaringBitmap
public static class BitmapUDF extends ScalarFunction {
    public byte[] eval(Integer... values) {
        RoaringBitmap32 bitmap = new RoaringBitmap32();
        for (Integer value : values) {
            if (value != null) {
                bitmap.add(value);
            }
        }
        return bitmap.serialize();
    }
}

// BitmapCountUDF - Gets cardinality from serialized RoaringBitmap
public static class BitmapCountUDF extends ScalarFunction {
    public Long eval(byte[] bitmapBytes) {
        if (bitmapBytes == null) {
            return 0L;
        }
        try {
            RoaringBitmap32 bitmap = new RoaringBitmap32();
            bitmap.deserialize(ByteBuffer.wrap(bitmapBytes));
            return bitmap.getCardinality();
        } catch (IOException e) {
            throw new RuntimeException("Failed to deserialize bitmap", e);
        }
    }
}

// BitmapContainsUDF - Checks if value exists in bitmap
public static class BitmapContainsUDF extends ScalarFunction {
    public Boolean eval(byte[] bitmapBytes, Integer value) {
        if (bitmapBytes == null || value == null) {
            return false;
        }
        try {
            RoaringBitmap32 bitmap = new RoaringBitmap32();
            bitmap.deserialize(ByteBuffer.wrap(bitmapBytes));
            return bitmap.contains(value);
        } catch (IOException e) {
            throw new RuntimeException("Failed to deserialize bitmap", e);
        }
    }
}

nested_update #

The nested_update function collects multiple rows into one array (so-called ‘nested table’). It supports ARRAY data types.

Use fields.<field-name>.nested-key=pk0,pk1,... to specify the primary keys of the nested table. If no keys, row will be appended to array.

An example:

-- orders table
CREATE TABLE orders (
  order_id BIGINT PRIMARY KEY NOT ENFORCED,
  user_name STRING,
  address STRING
);

-- sub orders that have the same order_id 
-- belongs to the same order
CREATE TABLE sub_orders (
  order_id BIGINT,
  sub_order_id INT,
  product_name STRING,
  price BIGINT,
  PRIMARY KEY (order_id, sub_order_id) NOT ENFORCED
);

-- wide table
CREATE TABLE order_wide (
  order_id BIGINT PRIMARY KEY NOT ENFORCED,
  user_name STRING,
  address STRING,
  sub_orders ARRAY<ROW<sub_order_id BIGINT, product_name STRING, price BIGINT>>
) WITH (
  'merge-engine' = 'aggregation',
  'fields.sub_orders.aggregate-function' = 'nested_update',
  'fields.sub_orders.nested-key' = 'sub_order_id'
);

-- widen
INSERT INTO order_wide

SELECT 
  order_id, 
  user_name,
  address, 
  CAST (NULL AS ARRAY<ROW<sub_order_id BIGINT, product_name STRING, price BIGINT>>) 
FROM orders

UNION ALL 
  
SELECT 
  order_id, 
  CAST (NULL AS STRING), 
  CAST (NULL AS STRING), 
  ARRAY[ROW(sub_order_id, product_name, price)] 
FROM sub_orders;

-- query using UNNEST
SELECT order_id, user_name, address, sub_order_id, product_name, price 
FROM order_wide, UNNEST(sub_orders) AS so(sub_order_id, product_name, price)

collect #

The collect function collects elements into an Array. You can set fields.<field-name>.distinct=true to deduplicate elements. It only supports ARRAY type.

merge_map #

The merge_map function merge input maps. It only supports MAP type.

Types of cardinality sketches #

Paimon uses the Apache DataSketches library of stochastic streaming algorithms to implement sketch modules. The DataSketches library includes various types of sketches, each one designed to solve a different sort of problem. Paimon supports HyperLogLog (HLL) and Theta cardinality sketches.

HyperLogLog #

The HyperLogLog (HLL) sketch aggregator is a very compact sketch algorithm for approximate distinct counting. You can also use the HLL aggregator to calculate a union of HLL sketches.

Theta #

The Theta sketch is a sketch algorithm for approximate distinct counting with set operations. Theta sketches let you count the overlap between sets, so that you can compute the union, intersection, or set difference between sketch objects.

Choosing a sketch type #

HLL and Theta sketches both support approximate distinct counting; however, the HLL sketch produces more accurate results and consumes less storage space. Theta sketches are more flexible but require significantly more memory.

When choosing an approximation algorithm for your use case, consider the following:

If your use case entails distinct counting and merging sketch objects, use the HLL sketch. If you need to evaluate union, intersection, or difference set operations, use the Theta sketch. You cannot merge HLL sketches with Theta sketches.

hll_sketch #

The hll_sketch function aggregates multiple serialized Sketch objects into a single Sketch. It supports VARBINARY data type.

An example:

-- source table
CREATE TABLE VISITS (
  id INT PRIMARY KEY NOT ENFORCED,
  user_id STRING
);

-- agg table
CREATE TABLE UV_AGG (
  id INT PRIMARY KEY NOT ENFORCED,
  uv VARBINARY
) WITH (
  'merge-engine' = 'aggregation',
  'fields.uv.aggregate-function' = 'hll_sketch'
);

-- Register the following class as a Flink function with the name "HLL_SKETCH" 
-- for example: create TEMPORARY function HLL_SKETCH as  'HllSketchFunction';
-- which is used to transform input to sketch bytes array:
--
-- public static class HllSketchFunction extends ScalarFunction {
--   public byte[] eval(String user_id) {
--     HllSketch hllSketch = new HllSketch();
--     hllSketch.update(user_id);
--     return hllSketch.toCompactByteArray();
--   }
-- }
--
INSERT INTO UV_AGG SELECT id, HLL_SKETCH(user_id) FROM VISITS;

-- Register the following class as a Flink function with the name "HLL_SKETCH_COUNT"
-- for example: create TEMPORARY function HLL_SKETCH_COUNT as  'HllSketchCountFunction';
-- which is used to get cardinality from sketch bytes array:
-- 
-- public static class HllSketchCountFunction extends ScalarFunction { 
--   public Double eval(byte[] sketchBytes) {
--     if (sketchBytes == null) {
--       return 0d; 
--     } 
--     return HllSketch.heapify(sketchBytes).getEstimate(); 
--   } 
-- }
--
-- Then we can get user cardinality based on the aggregated field.
SELECT id, HLL_SKETCH_COUNT(UV) as uv FROM UV_AGG;

theta_sketch #

The theta_sketch function aggregates multiple serialized Sketch objects into a single Sketch. It supports VARBINARY data type.

An example:

-- source table
CREATE TABLE VISITS (
  id INT PRIMARY KEY NOT ENFORCED,
  user_id STRING
);

-- agg table
CREATE TABLE UV_AGG (
  id INT PRIMARY KEY NOT ENFORCED,
  uv VARBINARY
) WITH (
  'merge-engine' = 'aggregation',
  'fields.uv.aggregate-function' = 'theta_sketch'
);

-- Register the following class as a Flink function with the name "THETA_SKETCH" 
-- for example: create TEMPORARY function THETA_SKETCH as  'ThetaSketchFunction';
-- which is used to transform input to sketch bytes array:
--
-- public static class ThetaSketchFunction extends ScalarFunction {
--   public byte[] eval(String user_id) {
--     UpdateSketch updateSketch = UpdateSketch.builder().build();
--     updateSketch.update(user_id);
--     return updateSketch.compact().toByteArray();
--   }
-- }
--
INSERT INTO UV_AGG SELECT id, THETA_SKETCH(user_id) FROM VISITS;

-- Register the following class as a Flink function with the name "THETA_SKETCH_COUNT"
-- for example: create TEMPORARY function THETA_SKETCH_COUNT as  'ThetaSketchCountFunction';
-- which is used to get cardinality from sketch bytes array:
-- 
-- public static class ThetaSketchCountFunction extends ScalarFunction { 
--   public Double eval(byte[] sketchBytes) {
--     if (sketchBytes == null) {
--       return 0d; 
--     } 
--     return Sketches.wrapCompactSketch(Memory.wrap(sketchBytes)).getEstimate(); 
--   } 
-- }
--
-- Then we can get user cardinality based on the aggregated field.
SELECT id, THETA_SKETCH_COUNT(UV) as uv FROM UV_AGG;
For streaming queries, aggregation merge engine must be used together with lookup or full-compaction changelog producer. (‘input’ changelog producer is also supported, but only returns input records.)

Retraction #

Only sum, product, collect, merge_map, nested_update, last_value and last_non_null_value supports retraction (UPDATE_BEFORE and DELETE), others aggregate functions do not support retraction. If you allow some functions to ignore retraction messages, you can configure: 'fields.${field_name}.ignore-retract'='true'.

The last_value and last_non_null_value just set field to null when accept retract messages.

The product will return null for retraction message when accumulator is null.

The collect and merge_map make a best-effort attempt to handle retraction messages, but the results are not guaranteed to be accurate. The following behaviors may occur when processing retraction messages:

  1. It might fail to handle retraction messages if records are disordered. For example, the table uses collect, and the upstreams send +I['A', 'B'] and -U['A'] respectively. If the table receives -U['A'] first, it can do nothing; then it receives +I['A', 'B'], the merge result will be +I['A', 'B'] instead of +I['B'].

  2. The retract message from one upstream will retract the result merged from multiple upstreams. For example, the table uses merge_map, and one upstream sends +I[1->A], another upstream sends +I[1->B], -D[1->B] later. The table will merge two insert values to +I[1->B] first, and then the -D[1->B] will retract the whole result, so the final result is an empty map instead of +I[1->A]

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