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Spark Writes🔗

To use Iceberg in Spark, first configure Spark catalogs.

Some plans are only available when using Iceberg SQL extensions in Spark 3.

Iceberg uses Apache Spark's DataSourceV2 API for data source and catalog implementations. Spark DSv2 is an evolving API with different levels of support in Spark versions:

Feature support Spark 3 Notes
SQL insert into ✔️ ⚠ Requires spark.sql.storeAssignmentPolicy=ANSI (default since Spark 3.0)
SQL merge into ✔️ ⚠ Requires Iceberg Spark extensions
SQL insert overwrite ✔️ ⚠ Requires spark.sql.storeAssignmentPolicy=ANSI (default since Spark 3.0)
SQL delete from ✔️ ⚠ Row-level delete requires Iceberg Spark extensions
SQL update ✔️ ⚠ Requires Iceberg Spark extensions
DataFrame append ✔️
DataFrame overwrite ✔️
DataFrame CTAS and RTAS ✔️ ⚠ Requires DSv2 API

Writing with SQL🔗

Spark 3 supports SQL INSERT INTO, MERGE INTO, and INSERT OVERWRITE, as well as the new DataFrameWriterV2 API.


To append new data to a table, use INSERT INTO.

INSERT INTO prod.db.table VALUES (1, 'a'), (2, 'b')
INSERT INTO prod.db.table SELECT ...


Spark 3 added support for MERGE INTO queries that can express row-level updates.

Iceberg supports MERGE INTO by rewriting data files that contain rows that need to be updated in an overwrite commit.

MERGE INTO is recommended instead of INSERT OVERWRITE because Iceberg can replace only the affected data files, and because the data overwritten by a dynamic overwrite may change if the table's partitioning changes.

MERGE INTO syntax🔗

MERGE INTO updates a table, called the target table, using a set of updates from another query, called the source. The update for a row in the target table is found using the ON clause that is like a join condition.

MERGE INTO t   -- a target table
USING (SELECT ...) s          -- the source updates
ON =                -- condition to find updates for target rows
WHEN ...                      -- updates

Updates to rows in the target table are listed using WHEN MATCHED ... THEN .... Multiple MATCHED clauses can be added with conditions that determine when each match should be applied. The first matching expression is used.

WHEN MATCHED AND t.count IS NULL AND s.op = 'increment' THEN UPDATE SET t.count = 0
WHEN MATCHED AND s.op = 'increment' THEN UPDATE SET t.count = t.count + 1

Source rows (updates) that do not match can be inserted:


Inserts also support additional conditions:

WHEN NOT MATCHED AND s.event_time > still_valid_threshold THEN INSERT (id, count) VALUES (, 1)

Only one record in the source data can update any given row of the target table, or else an error will be thrown.


INSERT OVERWRITE can replace data in the table with the result of a query. Overwrites are atomic operations for Iceberg tables.

The partitions that will be replaced by INSERT OVERWRITE depends on Spark's partition overwrite mode and the partitioning of a table. MERGE INTO can rewrite only affected data files and has more easily understood behavior, so it is recommended instead of INSERT OVERWRITE.

Overwrite behavior🔗

Spark's default overwrite mode is static, but dynamic overwrite mode is recommended when writing to Iceberg tables. Static overwrite mode determines which partitions to overwrite in a table by converting the PARTITION clause to a filter, but the PARTITION clause can only reference table columns.

Dynamic overwrite mode is configured by setting spark.sql.sources.partitionOverwriteMode=dynamic.

To demonstrate the behavior of dynamic and static overwrites, consider a logs table defined by the following DDL:

CREATE TABLE prod.my_app.logs (
    uuid string NOT NULL,
    level string NOT NULL,
    ts timestamp NOT NULL,
    message string)
USING iceberg
PARTITIONED BY (level, hours(ts))

Dynamic overwrite🔗

When Spark's overwrite mode is dynamic, partitions that have rows produced by the SELECT query will be replaced.

For example, this query removes duplicate log events from the example logs table.

INSERT OVERWRITE prod.my_app.logs
SELECT uuid, first(level), first(ts), first(message)
FROM prod.my_app.logs
WHERE cast(ts as date) = '2020-07-01'

In dynamic mode, this will replace any partition with rows in the SELECT result. Because the date of all rows is restricted to 1 July, only hours of that day will be replaced.

Static overwrite🔗

When Spark's overwrite mode is static, the PARTITION clause is converted to a filter that is used to delete from the table. If the PARTITION clause is omitted, all partitions will be replaced.

Because there is no PARTITION clause in the query above, it will drop all existing rows in the table when run in static mode, but will only write the logs from 1 July.

To overwrite just the partitions that were loaded, add a PARTITION clause that aligns with the SELECT query filter:

INSERT OVERWRITE prod.my_app.logs
PARTITION (level = 'INFO')
SELECT uuid, first(level), first(ts), first(message)
FROM prod.my_app.logs
WHERE level = 'INFO'

Note that this mode cannot replace hourly partitions like the dynamic example query because the PARTITION clause can only reference table columns, not hidden partitions.


Spark 3 added support for DELETE FROM queries to remove data from tables.

Delete queries accept a filter to match rows to delete.

DELETE FROM prod.db.table
WHERE ts >= '2020-05-01 00:00:00' and ts < '2020-06-01 00:00:00'

DELETE FROM prod.db.all_events
WHERE session_time < (SELECT min(session_time) FROM prod.db.good_events)

DELETE FROM prod.db.orders AS t1
WHERE EXISTS (SELECT oid FROM prod.db.returned_orders WHERE t1.oid = oid)

If the delete filter matches entire partitions of the table, Iceberg will perform a metadata-only delete. If the filter matches individual rows of a table, then Iceberg will rewrite only the affected data files.


Update queries accept a filter to match rows to update.

UPDATE prod.db.table
SET c1 = 'update_c1', c2 = 'update_c2'
WHERE ts >= '2020-05-01 00:00:00' and ts < '2020-06-01 00:00:00'

UPDATE prod.db.all_events
SET session_time = 0, ignored = true
WHERE session_time < (SELECT min(session_time) FROM prod.db.good_events)

UPDATE prod.db.orders AS t1
SET order_status = 'returned'
WHERE EXISTS (SELECT oid FROM prod.db.returned_orders WHERE t1.oid = oid)

For more complex row-level updates based on incoming data, see the section on MERGE INTO.

Writing to Branches🔗

Branch writes can be performed via SQL by providing a branch identifier, branch_yourBranch in the operation. Branch writes can also be performed as part of a write-audit-publish (WAP) workflow by specifying the spark.wap.branch config. Note WAP branch and branch identifier cannot both be specified. Also, the branch must exist before performing the write. The operation does not create the branch if it does not exist. For more information on branches please refer to branches.

-- INSERT (1,' a') (2, 'b') into the audit branch.
INSERT INTO prod.db.table.branch_audit VALUES (1, 'a'), (2, 'b');

-- MERGE INTO audit branch
MERGE INTO prod.db.table.branch_audit t 
USING (SELECT ...) s        
ON =          
WHEN ...

-- UPDATE audit branch
UPDATE prod.db.table.branch_audit AS t1
SET val = 'c'

-- DELETE FROM audit branch
DELETE FROM prod.dbl.table.branch_audit WHERE id = 2;

-- WAP Branch write
SET spark.wap.branch = audit-branch
INSERT INTO prod.db.table VALUES (3, 'c');

Writing with DataFrames🔗

Spark 3 introduced the new DataFrameWriterV2 API for writing to tables using data frames. The v2 API is recommended for several reasons:

  • CTAS, RTAS, and overwrite by filter are supported
  • All operations consistently write columns to a table by name
  • Hidden partition expressions are supported in partitionedBy
  • Overwrite behavior is explicit, either dynamic or by a user-supplied filter
  • The behavior of each operation corresponds to SQL statements
    • df.writeTo(t).create() is equivalent to CREATE TABLE AS SELECT
    • df.writeTo(t).replace() is equivalent to REPLACE TABLE AS SELECT
    • df.writeTo(t).append() is equivalent to INSERT INTO
    • df.writeTo(t).overwritePartitions() is equivalent to dynamic INSERT OVERWRITE

The v1 DataFrame write API is still supported, but is not recommended.


When writing with the v1 DataFrame API in Spark 3, use saveAsTable or insertInto to load tables with a catalog. Using format("iceberg") loads an isolated table reference that will not automatically refresh tables used by queries.

Appending data🔗

To append a dataframe to an Iceberg table, use append:

val data: DataFrame = ...

Overwriting data🔗

To overwrite partitions dynamically, use overwritePartitions():

val data: DataFrame = ...

To explicitly overwrite partitions, use overwrite to supply a filter:

data.writeTo("prod.db.table").overwrite($"level" === "INFO")

Creating tables🔗

To run a CTAS or RTAS, use create, replace, or createOrReplace operations:

val data: DataFrame = ...

If you have replaced the default Spark catalog (spark_catalog) with Iceberg's SparkSessionCatalog, do:

val data: DataFrame = ...

Create and replace operations support table configuration methods, like partitionedBy and tableProperty:

    .tableProperty("write.format.default", "orc")
    .partitionedBy($"level", days($"ts"))

The Iceberg table location can also be specified by the location table property:

    .tableProperty("location", "/path/to/location")

Schema Merge🔗

While inserting or updating Iceberg is capable of resolving schema mismatch at runtime. If configured, Iceberg will perform an automatic schema evolution as follows:

  • A new column is present in the source but not in the target table.

The new column is added to the target table. Column values are set to NULL in all the rows already present in the table

  • A column is present in the target but not in the source.

The target column value is set to NULL when inserting or left unchanged when updating the row.

The target table must be configured to accept any schema change by setting the property write.spark.accept-any-schema to true.

The writer must enable the mergeSchema option.


Writing Distribution Modes🔗

Iceberg's default Spark writers require that the data in each spark task is clustered by partition values. This distribution is required to minimize the number of file handles that are held open while writing. By default, starting in Iceberg 1.2.0, Iceberg also requests that Spark pre-sort data to be written to fit this distribution. The request to Spark is done through the table property write.distribution-mode with the value hash.

Let's go through writing the data against below sample table:

CREATE TABLE prod.db.sample (
    id bigint,
    data string,
    category string,
    ts timestamp)
USING iceberg
PARTITIONED BY (days(ts), category)

To write data to the sample table, data needs to be sorted by days(ts), category but this is taken care of automatically by the default hash distribution. Previously this would have required manually sorting, but this is no longer the case.

INSERT INTO prod.db.sample
SELECT id, data, category, ts FROM another_table

There are 3 options for write.distribution-mode

  • none - This is the previous default for Iceberg.
    This mode does not request any shuffles or sort to be performed automatically by Spark. Because no work is done automatically by Spark, the data must be manually sorted by partition value. The data must be sorted either within each spark task, or globally within the entire dataset. A global sort will minimize the number of output files.
    A sort can be avoided by using the Spark write fanout property but this will cause all file handles to remain open until each write task has completed.
  • hash - This mode is the new default and requests that Spark uses a hash-based exchange to shuffle the incoming write data before writing.
    Practically, this means that each row is hashed based on the row's partition value and then placed in a corresponding Spark task based upon that value. Further division and coalescing of tasks may take place because of Spark's Adaptive Query planning.
  • range - This mode requests that Spark perform a range based exchanged to shuffle the data before writing.
    This is a two stage procedure which is more expensive than the hash mode. The first stage samples the data to be written based on the partition and sort columns. The second stage uses the range information to shuffle the input data into Spark tasks. Each task gets an exclusive range of the input data which clusters the data by partition and also globally sorts.
    While this is more expensive than the hash distribution, the global ordering can be beneficial for read performance if sorted columns are used during queries. This mode is used by default if a table is created with a sort-order. Further division and coalescing of tasks may take place because of Spark's Adaptive Query planning.

Controlling File Sizes🔗

When writing data to Iceberg with Spark, it's important to note that Spark cannot write a file larger than a Spark task and a file cannot span an Iceberg partition boundary. This means although Iceberg will always roll over a file when it grows to, but unless the Spark task is large enough that will not happen. The size of the file created on disk will also be much smaller than the Spark task since the on disk data will be both compressed and in columnar format as opposed to Spark's uncompressed row representation. This means a 100 megabyte Spark task will create a file much smaller than 100 megabytes even if that task is writing to a single Iceberg partition. If the task writes to multiple partitions, the files will be even smaller than that.

To control what data ends up in each Spark task use a write distribution mode or manually repartition the data.

To adjust Spark's task size it is important to become familiar with Spark's various Adaptive Query Execution (AQE) parameters. When the write.distribution-mode is not none, AQE will control the coalescing and splitting of Spark tasks during the exchange to try to create tasks of spark.sql.adaptive.advisoryPartitionSizeInBytes size. These settings will also affect any user performed re-partitions or sorts. It is important again to note that this is the in-memory Spark row size and not the on disk columnar-compressed size, so a larger value than the target file size will need to be specified. The ratio of in-memory size to on disk size is data dependent. Future work in Spark should allow Iceberg to automatically adjust this parameter at write time to match the

Type compatibility🔗

Spark and Iceberg support different set of types. Iceberg does the type conversion automatically, but not for all combinations, so you may want to understand the type conversion in Iceberg in prior to design the types of columns in your tables.

Spark type to Iceberg type🔗

This type conversion table describes how Spark types are converted to the Iceberg types. The conversion applies on both creating Iceberg table and writing to Iceberg table via Spark.

Spark Iceberg Notes
boolean boolean
short integer
byte integer
integer integer
long long
float float
double double
date date
timestamp timestamp with timezone
timestamp_ntz timestamp without timezone
char string
varchar string
string string
binary binary
decimal decimal
struct struct
array list
map map


The table is based on representing conversion during creating table. In fact, broader supports are applied on write. Here're some points on write:

  • Iceberg numeric types (integer, long, float, double, decimal) support promotion during writes. e.g. You can write Spark types short, byte, integer, long to Iceberg type long.
  • You can write to Iceberg fixed type using Spark binary type. Note that assertion on the length will be performed.

Iceberg type to Spark type🔗

This type conversion table describes how Iceberg types are converted to the Spark types. The conversion applies on reading from Iceberg table via Spark.

Iceberg Spark Note
boolean boolean
integer integer
long long
float float
double double
date date
time Not supported
timestamp with timezone timestamp
timestamp without timezone timestamp_ntz
string string
uuid string
fixed binary
binary binary
decimal decimal
struct struct
list array
map map