Flink Queries

Iceberg support streaming and batch read With Apache Flink's DataStream API and Table API.

Reading with SQL

Iceberg support both streaming and batch read in Flink. Execute the following sql command to switch execution mode from streaming to batch, and vice versa:

-- Execute the flink job in streaming mode for current session context
SET execution.runtime-mode = streaming;

-- Execute the flink job in batch mode for current session context
SET execution.runtime-mode = batch;

Flink batch read

Submit a Flink batch job using the following sentences:

-- Execute the flink job in batch mode for current session context
SET execution.runtime-mode = batch;
SELECT * FROM sample;

Flink streaming read

Iceberg supports processing incremental data in Flink streaming jobs which starts from a historical snapshot-id:

-- Submit the flink job in streaming mode for current session.
SET execution.runtime-mode = streaming;

-- Enable this switch because streaming read SQL will provide few job options in flink SQL hint options.
SET table.dynamic-table-options.enabled=true;

-- Read all the records from the iceberg current snapshot, and then read incremental data starting from that snapshot.
SELECT * FROM sample /*+ OPTIONS('streaming'='true', 'monitor-interval'='1s')*/ ;

-- Read all incremental data starting from the snapshot-id '3821550127947089987' (records from this snapshot will be excluded).
SELECT * FROM sample /*+ OPTIONS('streaming'='true', 'monitor-interval'='1s', 'start-snapshot-id'='3821550127947089987')*/ ;

There are some options that could be set in Flink SQL hint options for streaming job, see read options for details.

FLIP-27 source for SQL

Here are the SQL settings for the FLIP-27 source. All other SQL settings and options documented above are applicable to the FLIP-27 source.

-- Opt in the FLIP-27 source. Default is false.
SET table.exec.iceberg.use-flip27-source = true;

Reading branches and tags with SQL

Branch and tags can be read via SQL by specifying options. For more details refer to Flink Configuration

--- Read from branch b1
SELECT * FROM table /*+ OPTIONS('branch'='b1') */ ;

--- Read from tag t1
SELECT * FROM table /*+ OPTIONS('tag'='t1') */;

--- Incremental scan from tag t1 to tag t2
SELECT * FROM table /*+ OPTIONS('streaming'='true', 'monitor-interval'='1s', 'start-tag'='t1', 'end-tag'='t2') */;

Reading with DataStream

Iceberg support streaming or batch read in Java API now.

Batch Read

This example will read all records from iceberg table and then print to the stdout console in flink batch job:

StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironment();
TableLoader tableLoader = TableLoader.fromHadoopTable("hdfs://nn:8020/warehouse/path");
DataStream<RowData> batch = FlinkSource.forRowData()
     .env(env)
     .tableLoader(tableLoader)
     .streaming(false)
     .build();

// Print all records to stdout.
batch.print();

// Submit and execute this batch read job.
env.execute("Test Iceberg Batch Read");

Streaming read

This example will read incremental records which start from snapshot-id '3821550127947089987' and print to stdout console in flink streaming job:

StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironment();
TableLoader tableLoader = TableLoader.fromHadoopTable("hdfs://nn:8020/warehouse/path");
DataStream<RowData> stream = FlinkSource.forRowData()
     .env(env)
     .tableLoader(tableLoader)
     .streaming(true)
     .startSnapshotId(3821550127947089987L)
     .build();

// Print all records to stdout.
stream.print();

// Submit and execute this streaming read job.
env.execute("Test Iceberg Streaming Read");

There are other options that can be set, please see the FlinkSource#Builder.

Reading with DataStream (FLIP-27 source)

FLIP-27 source interface was introduced in Flink 1.12. It aims to solve several shortcomings of the old SourceFunction streaming source interface. It also unifies the source interfaces for both batch and streaming executions. Most source connectors (like Kafka, file) in Flink repo have migrated to the FLIP-27 interface. Flink is planning to deprecate the old SourceFunction interface in the near future.

A FLIP-27 based Flink IcebergSource is added in iceberg-flink module. The FLIP-27 IcebergSource is currently an experimental feature.

Batch Read

This example will read all records from iceberg table and then print to the stdout console in flink batch job:

StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironment();
TableLoader tableLoader = TableLoader.fromHadoopTable("hdfs://nn:8020/warehouse/path");

IcebergSource<RowData> source = IcebergSource.forRowData()
    .tableLoader(tableLoader)
    .assignerFactory(new SimpleSplitAssignerFactory())
    .build();

DataStream<RowData> batch = env.fromSource(
    source,
    WatermarkStrategy.noWatermarks(),
    "My Iceberg Source",
    TypeInformation.of(RowData.class));

// Print all records to stdout.
batch.print();

// Submit and execute this batch read job.
env.execute("Test Iceberg Batch Read");

Streaming read

This example will start the streaming read from the latest table snapshot (inclusive). Every 60s, it polls Iceberg table to discover new append-only snapshots. CDC read is not supported yet.

StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironment();
TableLoader tableLoader = TableLoader.fromHadoopTable("hdfs://nn:8020/warehouse/path");

IcebergSource source = IcebergSource.forRowData()
    .tableLoader(tableLoader)
    .assignerFactory(new SimpleSplitAssignerFactory())
    .streaming(true)
    .streamingStartingStrategy(StreamingStartingStrategy.INCREMENTAL_FROM_LATEST_SNAPSHOT)
    .monitorInterval(Duration.ofSeconds(60))
    .build();

DataStream<RowData> stream = env.fromSource(
    source,
    WatermarkStrategy.noWatermarks(),
    "My Iceberg Source",
    TypeInformation.of(RowData.class));

// Print all records to stdout.
stream.print();

// Submit and execute this streaming read job.
env.execute("Test Iceberg Streaming Read");

There are other options that could be set by Java API, please see the IcebergSource#Builder.

Reading branches and tags with DataStream

Branches and tags can also be read via the DataStream API

StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironment();
TableLoader tableLoader = TableLoader.fromHadoopTable("hdfs://nn:8020/warehouse/path");
// Read from branch
DataStream<RowData> batch = FlinkSource.forRowData()
    .env(env)
    .tableLoader(tableLoader)
    .branch("test-branch")
    .streaming(false)
    .build();

// Read from tag
DataStream<RowData> batch = FlinkSource.forRowData()
    .env(env)
    .tableLoader(tableLoader)
    .tag("test-tag")
    .streaming(false)
    .build();

// Streaming read from start-tag
DataStream<RowData> batch = FlinkSource.forRowData()
    .env(env)
    .tableLoader(tableLoader)
    .streaming(true)
    .startTag("test-tag")
    .build();

Read as Avro GenericRecord

FLIP-27 Iceberg source provides AvroGenericRecordReaderFunction that converts Flink RowData Avro GenericRecord. You can use the convert to read from Iceberg table as Avro GenericRecord DataStream.

Please make sure flink-avro jar is included in the classpath. Also iceberg-flink-runtime shaded bundle jar can't be used because the runtime jar shades the avro package. Please use non-shaded iceberg-flink jar instead.

TableLoader tableLoader = ...;
Table table;
try (TableLoader loader = tableLoader) {
    loader.open();
    table = loader.loadTable();
}

AvroGenericRecordReaderFunction readerFunction = AvroGenericRecordReaderFunction.fromTable(table);

IcebergSource<GenericRecord> source =
    IcebergSource.<GenericRecord>builder()
        .tableLoader(tableLoader)
        .readerFunction(readerFunction)
        .assignerFactory(new SimpleSplitAssignerFactory())
        ...
        .build();

DataStream<Row> stream = env.fromSource(source, WatermarkStrategy.noWatermarks(),
    "Iceberg Source as Avro GenericRecord", new GenericRecordAvroTypeInfo(avroSchema));

Emitting watermarks

Emitting watermarks from the source itself could be beneficial for several purposes, like harnessing the Flink Watermark Alignment, or prevent triggering windows too early when reading multiple data files concurrently.

Enable watermark generation for an IcebergSource by setting the watermarkColumn. The supported column types are timestamp, timestamptz and long. Iceberg timestamp or timestamptz inherently contains the time precision. So there is no need to specify the time unit. But long type column doesn't contain time unit information. Use
watermarkTimeUnit to configure the conversion for long columns.

The watermarks are generated based on column metrics stored for data files and emitted once per split. If multiple smaller files with different time ranges are combined into a single split, it can increase the out-of-orderliness and extra data buffering in the Flink state. The main purpose of watermark alignment is to reduce out-of-orderliness and excess data buffering in the Flink state. Hence it is recommended to set read.split.open-file-cost to a very large value to prevent combining multiple smaller files into a single split. The negative impact (of not combining small files into a single split) is on read throughput, especially if there are many small files. In typical stateful processing jobs, source read throughput is not the bottleneck. Hence this is probably a reasonable tradeoff.

This feature requires column-level min-max stats. Make sure stats are generated for the watermark column during write phase. By default, the column metrics are collected for the first 100 columns of the table. If watermark column doesn't have stats enabled by default, use write properties starting with write.metadata.metrics when needed.

The following example could be useful if watermarks are used for windowing. The source reads Iceberg data files in order, using a timestamp column and emits watermarks:

StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironment();
TableLoader tableLoader = TableLoader.fromHadoopTable("hdfs://nn:8020/warehouse/path");

DataStream<RowData> stream =
    env.fromSource(
        IcebergSource.forRowData()
            .tableLoader(tableLoader)
            // Watermark using timestamp column
            .watermarkColumn("timestamp_column")
            .build(),
        // Watermarks are generated by the source, no need to generate it manually
        WatermarkStrategy.<RowData>noWatermarks()
            // Extract event timestamp from records
            .withTimestampAssigner((record, eventTime) -> record.getTimestamp(pos, precision).getMillisecond()),
        SOURCE_NAME,
        TypeInformation.of(RowData.class));

Example for reading Iceberg table using a long event column for watermark alignment:

StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironment();
TableLoader tableLoader = TableLoader.fromHadoopTable("hdfs://nn:8020/warehouse/path");

DataStream<RowData> stream =
    env.fromSource(
        IcebergSource source = IcebergSource.forRowData()
            .tableLoader(tableLoader)
            // Disable combining multiple files to a single split 
            .set(FlinkReadOptions.SPLIT_FILE_OPEN_COST, String.valueOf(TableProperties.SPLIT_SIZE_DEFAULT))
            // Watermark using long column
            .watermarkColumn("long_column")
            .watermarkTimeUnit(TimeUnit.MILLI_SCALE)
            .build(),
        // Watermarks are generated by the source, no need to generate it manually
        WatermarkStrategy.<RowData>noWatermarks()
            .withWatermarkAlignment(watermarkGroup, maxAllowedWatermarkDrift),
        SOURCE_NAME,
        TypeInformation.of(RowData.class));

Options

Read options

Flink read options are passed when configuring the Flink IcebergSource:

IcebergSource.forRowData()
    .tableLoader(TableLoader.fromCatalog(...))
    .assignerFactory(new SimpleSplitAssignerFactory())
    .streaming(true)
    .streamingStartingStrategy(StreamingStartingStrategy.INCREMENTAL_FROM_LATEST_SNAPSHOT)
    .startSnapshotId(3821550127947089987L)
    .monitorInterval(Duration.ofMillis(10L)) // or .set("monitor-interval", "10s") \ set(FlinkReadOptions.MONITOR_INTERVAL, "10s")
    .build()

For Flink SQL, read options can be passed in via SQL hints like this:

SELECT * FROM tableName /*+ OPTIONS('monitor-interval'='10s') */
...

Options can be passed in via Flink configuration, which will be applied to current session. Note that not all options support this mode.

env.getConfig()
    .getConfiguration()
    .set(FlinkReadOptions.SPLIT_FILE_OPEN_COST_OPTION, 1000L);
...

Check out all the options here: read-options

Inspecting tables

To inspect a table's history, snapshots, and other metadata, Iceberg supports metadata tables.

Metadata tables are identified by adding the metadata table name after the original table name. For example, history for db.table is read using db.table$history.

History

To show table history:

SELECT * FROM prod.db.table$history;

made_current_at	snapshot_id	parent_id	is_current_ancestor
2019-02-08 03:29:51.215	5781947118336215154	NULL	true
2019-02-08 03:47:55.948	5179299526185056830	5781947118336215154	true
2019-02-09 16:24:30.13	296410040247533544	5179299526185056830	false
2019-02-09 16:32:47.336	2999875608062437330	5179299526185056830	true
2019-02-09 19:42:03.919	8924558786060583479	2999875608062437330	true
2019-02-09 19:49:16.343	6536733823181975045	8924558786060583479	true

Info

This shows a commit that was rolled back. In this example, snapshot 296410040247533544 and 2999875608062437330 have the same parent snapshot 5179299526185056830. Snapshot 296410040247533544 was rolled back and is not an ancestor of the current table state.

Metadata Log Entries

To show table metadata log entries:

SELECT * from prod.db.table$metadata_log_entries;

timestamp	file	latest_snapshot_id	latest_schema_id	latest_sequence_number
2022-07-28 10:43:52.93	s3://.../table/metadata/00000-9441e604-b3c2-498a-a45a-6320e8ab9006.metadata.json	null	null	null
2022-07-28 10:43:57.487	s3://.../table/metadata/00001-f30823df-b745-4a0a-b293-7532e0c99986.metadata.json	170260833677645300	0	1
2022-07-28 10:43:58.25	s3://.../table/metadata/00002-2cc2837a-02dc-4687-acc1-b4d86ea486f4.metadata.json	958906493976709774	0	2

Snapshots

To show the valid snapshots for a table:

SELECT * FROM prod.db.table$snapshots;

committed_at	snapshot_id	parent_id	operation	manifest_list	summary
2019-02-08 03:29:51.215	57897183625154	null	append	s3://.../table/metadata/snap-57897183625154-1.avro	{ added-records -> 2478404, total-records -> 2478404, added-data-files -> 438, total-data-files -> 438, flink.job-id -> 2e274eecb503d85369fb390e8956c813 }

You can also join snapshots to table history. For example, this query will show table history, with the application ID that wrote each snapshot:

select
    h.made_current_at,
    s.operation,
    h.snapshot_id,
    h.is_current_ancestor,
    s.summary['flink.job-id']
from prod.db.table$history h
join prod.db.table$snapshots s
  on h.snapshot_id = s.snapshot_id
order by made_current_at;

made_current_at	operation	snapshot_id	is_current_ancestor	summary[flink.job-id]
2019-02-08 03:29:51.215	append	57897183625154	true	2e274eecb503d85369fb390e8956c813

Files

To show a table's current data files:

SELECT * FROM prod.db.table$files;

content	file_path	file_format	spec_id	partition	record_count	file_size_in_bytes	column_sizes	value_counts	null_value_counts	nan_value_counts	lower_bounds	upper_bounds	key_metadata	split_offsets	equality_ids	sort_order_id
0	s3:/.../table/data/00000-3-8d6d60e8-d427-4809-bcf0-f5d45a4aad96.parquet	PARQUET	0	{1999-01-01, 01}	1	597	[1 -> 90, 2 -> 62]	[1 -> 1, 2 -> 1]	[1 -> 0, 2 -> 0]	[]	[1 -> , 2 -> c]	[1 -> , 2 -> c]	null	[4]	null	null
0	s3:/.../table/data/00001-4-8d6d60e8-d427-4809-bcf0-f5d45a4aad96.parquet	PARQUET	0	{1999-01-01, 02}	1	597	[1 -> 90, 2 -> 62]	[1 -> 1, 2 -> 1]	[1 -> 0, 2 -> 0]	[]	[1 -> , 2 -> b]	[1 -> , 2 -> b]	null	[4]	null	null
0	s3:/.../table/data/00002-5-8d6d60e8-d427-4809-bcf0-f5d45a4aad96.parquet	PARQUET	0	{1999-01-01, 03}	1	597	[1 -> 90, 2 -> 62]	[1 -> 1, 2 -> 1]	[1 -> 0, 2 -> 0]	[]	[1 -> , 2 -> a]	[1 -> , 2 -> a]	null	[4]	null	null

Manifests

To show a table's current file manifests:

SELECT * FROM prod.db.table$manifests;

path	length	partition_spec_id	added_snapshot_id	added_data_files_count	existing_data_files_count	deleted_data_files_count	partition_summaries
s3://.../table/metadata/45b5290b-ee61-4788-b324-b1e2735c0e10-m0.avro	4479	0	6668963634911763636	8	0	0	[[false,null,2019-05-13,2019-05-15]]

Note:

1. Fields within partition_summaries column of the manifests table correspond to field_summary structs within manifest list, with the following order:
   • contains_null
   • contains_nan
   • lower_bound
   • upper_bound
2. contains_nan could return null, which indicates that this information is not available from the file's metadata. This usually occurs when reading from V1 table, where contains_nan is not populated.

Partitions

To show a table's current partitions:

SELECT * FROM prod.db.table$partitions;

partition	spec_id	record_count	file_count	total_data_file_size_in_bytes	position_delete_record_count	position_delete_file_count	equality_delete_record_count	equality_delete_file_count	last_updated_at(μs)	last_updated_snapshot_id
{20211001, 11}	0	1	1	100	2	1	0	0	1633086034192000	9205185327307503337
{20211002, 11}	0	4	3	500	1	1	0	0	1633172537358000	867027598972211003
{20211001, 10}	0	7	4	700	0	0	0	0	1633082598716000	3280122546965981531
{20211002, 10}	0	3	2	400	0	0	1	1	1633169159489000	6941468797545315876

Note: For unpartitioned tables, the partitions table will not contain the partition and spec_id fields.

All Metadata Tables

These tables are unions of the metadata tables specific to the current snapshot, and return metadata across all snapshots.

Danger

The "all" metadata tables may produce more than one row per data file or manifest file because metadata files may be part of more than one table snapshot.

All Data Files

To show all of the table's data files and each file's metadata:

SELECT * FROM prod.db.table$all_data_files;

content	file_path	file_format	partition	record_count	file_size_in_bytes	column_sizes	value_counts	null_value_counts	nan_value_counts	lower_bounds	upper_bounds	key_metadata	split_offsets	equality_ids	sort_order_id
0	s3://.../dt=20210102/00000-0-756e2512-49ae-45bb-aae3-c0ca475e7879-00001.parquet	PARQUET	{20210102}	14	2444	{1 -> 94, 2 -> 17}	{1 -> 14, 2 -> 14}	{1 -> 0, 2 -> 0}	{}	{1 -> 1, 2 -> 20210102}	{1 -> 2, 2 -> 20210102}	null	[4]	null	0
0	s3://.../dt=20210103/00000-0-26222098-032f-472b-8ea5-651a55b21210-00001.parquet	PARQUET	{20210103}	14	2444	{1 -> 94, 2 -> 17}	{1 -> 14, 2 -> 14}	{1 -> 0, 2 -> 0}	{}	{1 -> 1, 2 -> 20210103}	{1 -> 3, 2 -> 20210103}	null	[4]	null	0
0	s3://.../dt=20210104/00000-0-a3bb1927-88eb-4f1c-bc6e-19076b0d952e-00001.parquet	PARQUET	{20210104}	14	2444	{1 -> 94, 2 -> 17}	{1 -> 14, 2 -> 14}	{1 -> 0, 2 -> 0}	{}	{1 -> 1, 2 -> 20210104}	{1 -> 3, 2 -> 20210104}	null	[4]	null	0

All Manifests

To show all of the table's manifest files:

SELECT * FROM prod.db.table$all_manifests;

path	length	partition_spec_id	added_snapshot_id	added_data_files_count	existing_data_files_count	deleted_data_files_count	partition_summaries
s3://.../metadata/a85f78c5-3222-4b37-b7e4-faf944425d48-m0.avro	6376	0	6272782676904868561	2	0	0	[{false, false, 20210101, 20210101}]

Note:

1. Fields within partition_summaries column of the manifests table correspond to field_summary structs within manifest list, with the following order:
   • contains_null
   • contains_nan
   • lower_bound
   • upper_bound
2. contains_nan could return null, which indicates that this information is not available from the file's metadata. This usually occurs when reading from V1 table, where contains_nan is not populated.

References

To show a table's known snapshot references:

SELECT * FROM prod.db.table$refs;

name	type	snapshot_id	max_reference_age_in_ms	min_snapshots_to_keep	max_snapshot_age_in_ms
main	BRANCH	4686954189838128572	10	20	30
testTag	TAG	4686954189838128572	10	null	null