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Flink Reads

Fluss supports streaming and batch read with Apache Flink's SQL & Table API. Execute the following SQL command to switch execution mode from streaming to batch, and vice versa:

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

Streaming Read​

By default, Streaming read produces the latest snapshot on the table upon first startup, and continue to read the latest changes.

Fluss by default ensures that your startup is properly processed with all data included.

Fluss Source in streaming mode is unbounded, like a queue that never ends.

Flink SQL
SET 'execution.runtime-mode' = 'streaming';
Flink SQL
SELECT * FROM my_table ;

You can also do streaming read without reading the snapshot data, you can use latest scan mode, which only reads the changelogs (or logs) from the latest offset:

Flink SQL
SELECT * FROM my_table /*+ OPTIONS('scan.startup.mode' = 'latest') */;

Column Pruning​

Column pruning minimizes I/O by reading only the columns used in a query and ignoring unused ones at the storage layer. In Fluss, column pruning is implemented using Apache Arrow as the default log format to optimize streaming reads from Log Tables and change logs of PrimaryKey Tables. Benchmark results show that column pruning can reach 10x read performance improvement, and reduce unnecessary network traffic (reduce 80% I/O if 80% columns are not used).

note
  1. Column pruning is only available when the table uses the Arrow log format ('table.log.format' = 'arrow'), which is enabled by default.
  2. Reading log data from remote storage currently does not support column pruning.

Example​

1. Create a table

Flink SQL
CREATE TABLE `testcatalog`.`testdb`.`log_table` (
    `c_custkey` INT NOT NULL,
    `c_name` STRING NOT NULL,
    `c_address` STRING NOT NULL,
    `c_nationkey` INT NOT NULL,
    `c_phone` STRING NOT NULL,
    `c_acctbal` DECIMAL(15, 2) NOT NULL,
    `c_mktsegment` STRING NOT NULL,
    `c_comment` STRING NOT NULL
);

2. Query a single column:

Flink SQL
SELECT `c_name` FROM `testcatalog`.`testdb`.`log_table`;

3. Verify with EXPLAIN:

Flink SQL
EXPLAIN SELECT `c_name` FROM `testcatalog`.`testdb`.`log_table`;

Output:

== Optimized Execution Plan ==
TableSourceScan(table=[[testcatalog, testdb, log_table, project=[c_name]]], fields=[c_name])

This confirms that only the c_name column is being read from storage.

Partition Pruning​

Partition pruning is an optimization technique for Fluss partitioned tables. It reduces the number of partitions scanned during a query by filtering based on partition keys. This optimization is especially useful in streaming scenarios for Multi-Field Partitioned Tables that has many partitions. The partition pruning also supports dynamically pruning new created partitions during streaming read.

note
  1. Currently, only equality conditions (e.g., c_nationkey = 'US') are supported for partition pruning. Operators like <, >, OR, and IN are not yet supported.

Example​

1. Create a partitioned table:

Flink SQL
CREATE TABLE `testcatalog`.`testdb`.`log_partitioned_table` (
    `c_custkey` INT NOT NULL,
    `c_name` STRING NOT NULL,
    `c_address` STRING NOT NULL,
    `c_nationkey` STRING NOT NULL,
    `c_phone` STRING NOT NULL,
    `c_acctbal` DECIMAL(15, 2) NOT NULL,
    `c_mktsegment` STRING NOT NULL,
    `c_comment` STRING NOT NULL,
    `dt` STRING NOT NULL
) PARTITIONED BY (`c_nationkey`,`dt`);

2. Query with partition filter:

Flink SQL
SELECT * FROM `testcatalog`.`testdb`.`log_partitioned_table` WHERE `c_nationkey` = 'US';

Fluss source will scan only the partitions where c_nationkey = 'US'. For example, if the following partitions exist:

  • US,2025-06-13
  • China,2025-06-13
  • US,2025-06-14
  • China,2025-06-14

Only US,2025-06-13 and US,2025-06-14 will be read.

As new partitions like US,2025-06-15, China,2025-06-15 are created, partition US,2025-06-15 will be automatically included in the stream, while China,2025-06-15 will be dynamically filtered out based on the partition pruning condition.

3. Verify with EXPLAIN:

Flink SQL
EXPLAIN SELECT * FROM `testcatalog`.`testdb`.`log_partitioned_table` WHERE `c_nationkey` = 'US';

Output:

== Optimized Execution Plan ==
TableSourceScan(table=[[testcatalog, testdb, log_partitioned_table, filter=[=(c_nationkey, _UTF-16LE'US':VARCHAR(2147483647) CHARACTER SET "UTF-16LE")]]], fields=[c_custkey, c_name, c_address, c_nationkey, c_phone, c_acctbal, c_mktsegment, c_comment, dt])

This confirms that only partitions matching c_nationkey = 'US' will be scanned.

Batch Read​

Limit Read​

The Fluss source supports limiting reads for both primary-key tables and log tables, making it convenient to preview the latest N records in a table.

Example​

  1. Create a table and prepare data
Flink SQL
CREATE TABLE log_table (
    `c_custkey` INT NOT NULL,
    `c_name` STRING NOT NULL,
    `c_address` STRING NOT NULL,
    `c_nationkey` INT NOT NULL,
    `c_phone` STRING NOT NULL,
    `c_acctbal` DECIMAL(15, 2) NOT NULL,
    `c_mktsegment` STRING NOT NULL,
    `c_comment` STRING NOT NULL
);
Flink SQL
INSERT INTO log_table
VALUES (1, 'Customer1', 'IVhzIApeRb ot,c,E', 15, '25-989-741-2988', 711.56, 'BUILDING', 'comment1'),
       (2, 'Customer2', 'XSTf4,NCwDVaWNe6tEgvwfmRchLXak', 13, '23-768-687-3665', 121.65, 'AUTOMOBILE', 'comment2'),
       (3, 'Customer3', 'MG9kdTD2WBHm', 1, '11-719-748-3364', 7498.12, 'AUTOMOBILE', 'comment3');
;
  1. Query from table.
Flink SQL
-- Execute the flink job in batch mode for current session context
SET 'execution.runtime-mode' = 'batch';
Flink SQL
SET 'sql-client.execution.result-mode' = 'tableau';
Flink SQL
SELECT * FROM log_table LIMIT 10;

Point Query​

The Fluss source supports point queries for primary-key tables, allowing you to inspect specific records efficiently. Currently, this functionality is exclusive to primary-key tables.

Example​

  1. Create a table and prepare data
Flink SQL
CREATE TABLE pk_table (
    `c_custkey` INT NOT NULL,
    `c_name` STRING NOT NULL,
    `c_address` STRING NOT NULL,
    `c_nationkey` INT NOT NULL,
    `c_phone` STRING NOT NULL,
    `c_acctbal` DECIMAL(15, 2) NOT NULL,
    `c_mktsegment` STRING NOT NULL,
    `c_comment` STRING NOT NULL,
    PRIMARY KEY (c_custkey) NOT ENFORCED
);
Flink SQL
INSERT INTO pk_table
VALUES (1, 'Customer1', 'IVhzIApeRb ot,c,E', 15, '25-989-741-2988', 711.56, 'BUILDING', 'comment1'),
       (2, 'Customer2', 'XSTf4,NCwDVaWNe6tEgvwfmRchLXak', 13, '23-768-687-3665', 121.65, 'AUTOMOBILE', 'comment2'),
       (3, 'Customer3', 'MG9kdTD2WBHm', 1, '11-719-748-3364', 7498.12, 'AUTOMOBILE', 'comment3');
  1. Query from table.
Flink SQL
-- Execute the flink job in batch mode for current session context
SET 'execution.runtime-mode' = 'batch';
Flink SQL
SET 'sql-client.execution.result-mode' = 'tableau';
Flink SQL
SELECT * FROM pk_table WHERE c_custkey = 1;

Aggregations​

The Fluss source supports pushdown count aggregation for the log table in batch mode. It is useful to preview the total number of the log table;

Flink SQL
-- Execute the flink job in batch mode for current session context
SET 'execution.runtime-mode' = 'batch';
Flink SQL
SET 'sql-client.execution.result-mode' = 'tableau';
Flink SQL
SELECT COUNT(*) FROM log_table;

Read Options​

Start Reading Position​

The config option scan.startup.mode enables you to specify the starting point for data consumption. Fluss currently supports the following scan.startup.mode options:

  • full (default): For primary key tables, it first consumes the full data set and then consumes incremental data. For log tables, it starts consuming from the earliest offset.
  • earliest: For primary key tables, it starts consuming from the earliest changelog offset; for log tables, it starts consuming from the earliest log offset.
  • latest: For primary key tables, it starts consuming from the latest changelog offset; for log tables, it starts consuming from the latest log offset.
  • timestamp: For primary key tables, it starts consuming the changelog from a specified time (defined by the configuration item scan.startup.timestamp); for log tables, it starts consuming from the offset corresponding to the specified time.

You can dynamically apply the scan parameters via SQL hints. For instance, the following SQL statement temporarily sets the scan.startup.mode to latest when consuming the log_table table.

Flink SQL
SELECT * FROM log_table /*+ OPTIONS('scan.startup.mode' = 'latest') */;

Also, the following SQL statement temporarily sets the scan.startup.mode to timestamp when consuming the log_table table.

Flink SQL
-- timestamp mode with microseconds.
SELECT * FROM log_table
/*+ OPTIONS('scan.startup.mode' = 'timestamp',
'scan.startup.timestamp' = '1678883047356') */;
Flink SQL
-- timestamp mode with a time string format
SELECT * FROM log_table
/*+ OPTIONS('scan.startup.mode' = 'timestamp',
'scan.startup.timestamp' = '2023-12-09 23:09:12') */;