Writing Tables

Writing Tables #

You can use the INSERT statement to inserts new rows into a table or overwrites the existing data in the table. The inserted rows can be specified by value expressions or result from a query.

Syntax # 

INSERT { INTO | OVERWRITE } table_identifier [ part_spec ] [ column_list ] { value_expr | query };

  • part_spec

    An optional parameter that specifies a comma-separated list of key and value pairs for partitions. Note that one can use a typed literal (e.g., date’2019-01-02’) in the partition spec.

    Syntax: PARTITION ( partition_col_name = partition_col_val [ , … ] )

  • column_list

    An optional parameter that specifies a comma-separated list of columns belonging to the table_identifier table.

    Syntax: (col_name1 [, column_name2, …])

    All specified columns should exist in the table and not be duplicated from each other.
It includes all columns except the static partition columns.
  
The size of the column list should be exactly the size of the data from VALUES clause or query.

  • value_expr

    Specifies the values to be inserted. Either an explicitly specified value or a NULL can be inserted. A comma must be used to separate each value in the clause. More than one set of values can be specified to insert multiple rows.

    Syntax: VALUES ( { value | NULL } [ , … ] ) [ , ( … ) ]

    Currently, Flink doesn't support use NULL directly, so the NULL should be cast to actual 
data type by `CAST (NULL AS data_type)`.

For more information, please check the syntax document:

Flink INSERT Statement

Spark INSERT Statement

Write Nullable field to Not-null field #

We cannot insert into a non-null column of one table with a nullable column of another table. Assume that, we have a column key1 in table A which is primary key, primary key cannot be null. We have a column key2 in table B, which is nullable. If we run a sql like this:

INSERT INTO A key1 SELECT key2 FROM B

We will catch an exception,

  • In spark: “Cannot write nullable values to non-null column ‘key1’.”
  • In flink: “Column ‘key1’ is NOT NULL, however, a null value is being written into it. "

Other engines will throw respective exception to announce this. We can use function “NVL” or “COALESCE” to work around, turn a nullable column into a non-null column to escape exception:

INSERT INTO A key1 SELECT COALESCE(key2, <non-null expression>) FROM B;

Applying Records/Changes to Tables #

Use INSERT INTO to apply records and changes to tables.

INSERT INTO MyTable SELECT ...

Paimon supports shuffle data by partition and bucket in sink phase.

Use INSERT INTO to apply records and changes to tables.

INSERT INTO MyTable SELECT ...

Overwriting #

Note :If spark.sql.sources.partitionOverwriteMode is set to dynamic by default in Spark, in order to ensure that the insert overwrite function of the Paimon table can be used normally, spark.sql.extensions should be set to org.apache.paimon.spark.extensions.PaimonSparkSessionExtensions.

Overwriting the Whole Table #

For unpartitioned tables, Paimon supports overwriting the whole table.

Use INSERT OVERWRITE to overwrite the whole unpartitioned table.

INSERT OVERWRITE MyTable SELECT ...

INSERT OVERWRITE MyTable SELECT ...

Overwriting a Partition #

For partitioned tables, Paimon supports overwriting a partition.

Use INSERT OVERWRITE to overwrite a partition.

INSERT OVERWRITE MyTable PARTITION (key1 = value1, key2 = value2, ...) SELECT ...

INSERT OVERWRITE MyTable PARTITION (key1 = value1, key2 = value2, ...) SELECT ...

Dynamic Overwrite #

Flink’s default overwrite mode is dynamic partition overwrite (that means Paimon only deletes the partitions appear in the overwritten data). You can configure dynamic-partition-overwrite to change it to static overwritten.

-- MyTable is a Partitioned Table

-- Dynamic overwrite
INSERT OVERWRITE MyTable SELECT ...

-- Static overwrite (Overwrite whole table)
INSERT OVERWRITE MyTable /*+ OPTIONS('dynamic-partition-overwrite' = 'false') */ SELECT ...

Spark’s default overwrite mode is static partition overwrite. To enable dynamic overwritten needs these configs below:

--conf spark.sql.extensions=org.apache.paimon.spark.extensions.PaimonSparkSessionExtensions

-- MyTable is a Partitioned Table

-- Static overwrite (Overwrite whole table)
INSERT OVERWRITE MyTable SELECT ...

-- Dynamic overwrite
SET spark.sql.sources.partitionOverwriteMode=dynamic;
INSERT OVERWRITE MyTable SELECT ...

Truncate tables #

You can use INSERT OVERWRITE to purge tables by inserting empty value.

INSERT OVERWRITE MyTable /*+ OPTIONS('dynamic-partition-overwrite'='false') */ SELECT * FROM MyTable WHERE false;

TRUNCATE TABLE MyTable;

TRUNCATE TABLE MyTable;

Purging Partitions #

Currently, Paimon supports two ways to purge partitions.

  1. Like purging tables, you can use INSERT OVERWRITE to purge data of partitions by inserting empty value to them.

  2. Method #1 does not support to drop multiple partitions. In case that you need to drop multiple partitions, you can submit the drop_partition job through flink run.

-- Syntax
INSERT OVERWRITE MyTable /*+ OPTIONS('dynamic-partition-overwrite'='false') */ 
PARTITION (key1 = value1, key2 = value2, ...) SELECT selectSpec FROM MyTable WHERE false;

-- The following SQL is an example:
-- table definition
CREATE TABLE MyTable (
    k0 INT,
    k1 INT,
    v STRING
) PARTITIONED BY (k0, k1);

-- you can use
INSERT OVERWRITE MyTable /*+ OPTIONS('dynamic-partition-overwrite'='false') */ 
PARTITION (k0 = 0) SELECT k1, v FROM MyTable WHERE false;

-- or
INSERT OVERWRITE MyTable /*+ OPTIONS('dynamic-partition-overwrite'='false') */ 
PARTITION (k0 = 0, k1 = 0) SELECT v FROM MyTable WHERE false;

Run the following command to submit a drop_partition job for the table.

<FLINK_HOME>/bin/flink run \
    /path/to/paimon-flink-action-0.7.0-incubating.jar \
    drop_partition \
    --warehouse <warehouse-path> \
    --database <database-name> \
    --table <table-name> \
    [--partition <partition_spec> [--partition <partition_spec> ...]] \
    [--catalog_conf <paimon-catalog-conf> [--catalog_conf <paimon-catalog-conf> ...]]

partition_spec:
key1=value1,key2=value2...

For more information of drop_partition, see

<FLINK_HOME>/bin/flink run \
    /path/to/paimon-flink-action-0.7.0-incubating.jar \
    drop_partition --help

Updating tables #

Important table properties setting:

  1. Only primary key table supports this feature.
  2. MergeEngine needs to be deduplicate or partial-update to support this feature.
Warning: we do not support updating primary keys.

Currently, Paimon supports updating records by using UPDATE in Flink 1.17 and later versions. You can perform UPDATE in Flink’s batch mode.

-- Syntax
UPDATE table_identifier SET column1 = value1, column2 = value2, ... WHERE condition;

-- The following SQL is an example:
-- table definition
CREATE TABLE MyTable (
	a STRING,
	b INT,
	c INT,
	PRIMARY KEY (a) NOT ENFORCED
) WITH ( 
	'merge-engine' = 'deduplicate' 
);

-- you can use
UPDATE MyTable SET b = 1, c = 2 WHERE a = 'myTable';

To enable update needs these configs below:

--conf spark.sql.extensions=org.apache.paimon.spark.extensions.PaimonSparkSessionExtensions

spark supports update PrimitiveType and StructType, for example:

-- Syntax
UPDATE table_identifier SET column1 = value1, column2 = value2, ... WHERE condition;

CREATE TABLE T (
  id INT, 
  s STRUCT<c1: INT, c2: STRING>, 
  name STRING)
TBLPROPERTIES (
  'primary-key' = 'id', 
  'merge-engine' = 'deduplicate'
);

-- you can use
UPDATE T SET name = 'a_new' WHERE id = 1;
UPDATE T SET s.c2 = 'a_new' WHERE s.c1 = 1;

Deleting from table #

In Flink 1.16 and previous versions, Paimon only supports deleting records via submitting the ‘delete’ job through flink run.

Run the following command to submit a ‘delete’ job for the table.

<FLINK_HOME>/bin/flink run \
    /path/to/paimon-flink-action-0.7.0-incubating.jar \
    delete \
    --warehouse <warehouse-path> \
    --database <database-name> \
    --table <table-name> \
    --where <filter_spec> \
    [--catalog_conf <paimon-catalog-conf> [--catalog_conf <paimon-catalog-conf> ...]]
    
filter_spec is equal to the 'WHERE' clause in SQL DELETE statement. Examples:
    age >= 18 AND age <= 60
    animal <> 'cat'
    id > (SELECT count(*) FROM employee)

For more information of ‘delete’, see

<FLINK_HOME>/bin/flink run \
    /path/to/paimon-flink-action-0.7.0-incubating.jar \
    delete --help

Important table properties setting:

  1. Only primary key tables support this feature.
  2. If the table has primary keys, MergeEngine needs to be deduplicate to support this feature.
Warning: we do not support deleting from table in streaming mode. 
-- Syntax
DELETE FROM table_identifier WHERE conditions;

-- The following SQL is an example:
-- table definition
CREATE TABLE MyTable (
    id BIGINT NOT NULL,
    currency STRING,
    rate BIGINT,
    dt String,
    PRIMARY KEY (id, dt) NOT ENFORCED
) PARTITIONED BY (dt) WITH ( 
    'merge-engine' = 'deduplicate' 
);

-- you can use
DELETE FROM MyTable WHERE currency = 'UNKNOWN';

Important table properties setting:

  1. Only primary key tables support this feature.
  2. If the table has primary keys, MergeEngine needs to be deduplicate to support this feature.

To enable delete needs these configs below:

--conf spark.sql.extensions=org.apache.paimon.spark.extensions.PaimonSparkSessionExtensions

DELETE FROM MyTable WHERE currency = 'UNKNOWN';

Merging into table #

Paimon supports “MERGE INTO” via submitting the ‘merge_into’ job through flink run.

Important table properties setting:

  1. Only primary key table supports this feature.
  2. The action won’t produce UPDATE_BEFORE, so it’s not recommended to set ‘changelog-producer’ = ‘input’.

The design referenced such syntax:

MERGE INTO target-table
  USING source_table | source-expr AS source-alias
  ON merge-condition
  WHEN MATCHED [AND matched-condition]
    THEN UPDATE SET xxx
  WHEN MATCHED [AND matched-condition]
    THEN DELETE
  WHEN NOT MATCHED [AND not_matched_condition]
    THEN INSERT VALUES (xxx)
  WHEN NOT MATCHED BY SOURCE [AND not-matched-by-source-condition]
    THEN UPDATE SET xxx
  WHEN NOT MATCHED BY SOURCE [AND not-matched-by-source-condition]
    THEN DELETE

The merge_into action use “upsert” semantics instead of “update”, which means if the row exists, then do update, else do insert. For example, for non-primary-key table, you can update every column, but for primary key table, if you want to update primary keys, you have to insert a new row which has different primary keys from rows in the table. In this scenario, “upsert” is useful.

Run the following command to submit a ‘merge_into’ job for the table.

<FLINK_HOME>/bin/flink run \
    /path/to/paimon-flink-action-0.7.0-incubating.jar \
    merge_into \
    --warehouse <warehouse-path> \
    --database <database-name> \
    --table <target-table> \
    [--target_as <target-table-alias>] \
    --source_table <source_table-name> \
    [--source_sql <sql> ...]\
    --on <merge-condition> \
    --merge_actions <matched-upsert,matched-delete,not-matched-insert,not-matched-by-source-upsert,not-matched-by-source-delete> \
    --matched_upsert_condition <matched-condition> \
    --matched_upsert_set <upsert-changes> \
    --matched_delete_condition <matched-condition> \
    --not_matched_insert_condition <not-matched-condition> \
    --not_matched_insert_values <insert-values> \
    --not_matched_by_source_upsert_condition <not-matched-by-source-condition> \
    --not_matched_by_source_upsert_set <not-matched-upsert-changes> \
    --not_matched_by_source_delete_condition <not-matched-by-source-condition> \
    [--catalog_conf <paimon-catalog-conf> [--catalog_conf <paimon-catalog-conf> ...]]
    
You can pass sqls by '--source_sql <sql> [, --source_sql <sql> ...]' to config environment and create source table at runtime.
    
-- Examples:
-- Find all orders mentioned in the source table, then mark as important if the price is above 100 
-- or delete if the price is under 10.
./flink run \
    /path/to/paimon-flink-action-0.7.0-incubating.jar \
    merge_into \
    --warehouse <warehouse-path> \
    --database <database-name> \
    --table T \
    --source_table S \
    --on "T.id = S.order_id" \
    --merge_actions \
    matched-upsert,matched-delete \
    --matched_upsert_condition "T.price > 100" \
    --matched_upsert_set "mark = 'important'" \
    --matched_delete_condition "T.price < 10" 
    
-- For matched order rows, increase the price, and if there is no match, insert the order from the 
-- source table:
./flink run \
    /path/to/paimon-flink-action-0.7.0-incubating.jar \
    merge_into \
    --warehouse <warehouse-path> \
    --database <database-name> \
    --table T \
    --source_table S \
    --on "T.id = S.order_id" \
    --merge_actions \
    matched-upsert,not-matched-insert \
    --matched_upsert_set "price = T.price + 20" \
    --not_matched_insert_values * 

-- For not matched by source order rows (which are in the target table and does not match any row in the
-- source table based on the merge-condition), decrease the price or if the mark is 'trivial', delete them:
./flink run \
    /path/to/paimon-flink-action-0.7.0-incubating.jar \
    merge_into \
    --warehouse <warehouse-path> \
    --database <database-name> \
    --table T \
    --source_table S \
    --on "T.id = S.order_id" \
    --merge_actions \
    not-matched-by-source-upsert,not-matched-by-source-delete \
    --not_matched_by_source_upsert_condition "T.mark <> 'trivial'" \
    --not_matched_by_source_upsert_set "price = T.price - 20" \
    --not_matched_by_source_delete_condition "T.mark = 'trivial'"
    
-- A --source_sql example: 
-- Create a temporary view S in new catalog and use it as source table
./flink run \
    /path/to/paimon-flink-action-0.7.0-incubating.jar \
    merge_into \
    --warehouse <warehouse-path> \
    --database <database-name> \
    --table T \
    --source_sql "CREATE CATALOG test_cat WITH (...)" \
    --source_sql "CREATE TEMPORARY VIEW test_cat.`default`.S AS SELECT order_id, price, 'important' FROM important_order" \
    --source_table test_cat.default.S \
    --on "T.id = S.order_id" \
    --merge_actions not-matched-insert\
    --not_matched_insert_values *

The term ‘matched’ explanation:

  1. matched: changed rows are from target table and each can match a source table row based on merge-condition and optional matched-condition (source ∩ target).
  2. not matched: changed rows are from source table and all rows cannot match any target table row based on merge-condition and optional not_matched_condition (source - target).
  3. not matched by source: changed rows are from target table and all row cannot match any source table row based on merge-condition and optional not-matched-by-source-condition (target - source).

Parameters format:

  1. matched_upsert_changes:
    col = <source_table>.col | expression [, …] (Means setting <target_table>.col with given value. Do not add ‘<target_table>.’ before ‘col’.)
    Especially, you can use ‘*’ to set columns with all source columns (require target table’s schema is equal to source’s).
  2. not_matched_upsert_changes is similar to matched_upsert_changes, but you cannot reference source table’s column or use ‘*’.
  3. insert_values:
    col1, col2, …, col_end
    Must specify values of all columns. For each column, you can reference <source_table>.col or use an expression.
    Especially, you can use ‘*’ to insert with all source columns (require target table’s schema is equal to source’s).
  4. not_matched_condition cannot use target table’s columns to construct condition expression.
  5. not_matched_by_source_condition cannot use source table’s columns to construct condition expression.
  1. Target alias cannot be duplicated with existed table name.
  2. If the source table is not in the current catalog and current database, the source-table-name must be qualified (database.table or catalog.database.table if created a new catalog). For examples:
    (1) If source table ‘my_source’ is in ‘my_db’, qualify it:
    --source_table “my_db.my_source”
    (2) Example for sqls:
    When sqls changed current catalog and database, it’s OK to not qualify the source table name:
    --source_sql “CREATE CATALOG my_cat WITH (…)"
    --source_sql “USE CATALOG my_cat”
    --source_sql “CREATE DATABASE my_db”
    --source_sql “USE my_db”
    --source_sql “CREATE TABLE S …"
    --source_table S
    but you must qualify it in the following case:
    --source_sql “CREATE CATALOG my_cat WITH (…)"
    --source_sql “CREATE TABLE my_cat.`default`.S …"
    --source_table my_cat.default.S
    You can use just ‘S’ as source table name in following arguments.
  3. At least one merge action must be specified.
  4. If both matched-upsert and matched-delete actions are present, their conditions must both be present too (same to not-matched-by-source-upsert and not-matched-by-source-delete). Otherwise, all conditions are optional.
  5. All conditions, set changes and values should use Flink SQL syntax. To ensure the whole command runs normally in Shell, please quote them with "" to escape blank spaces and use ‘\’ to escape special characters in statement. For example:
    --source_sql “CREATE TABLE T (k INT) WITH (‘special-key’ = ‘123\!')”

For more information of ‘merge_into’, see

<FLINK_HOME>/bin/flink run \
    /path/to/paimon-flink-action-0.7.0-incubating.jar \
    merge_into --help
Edit This Page
Apache Paimon is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.
Copyright © 2023 The Apache Software Foundation. Apache Paimon, Paimon, and its feather logo are trademarks of The Apache Software Foundation.