Read data from StarRocks using Spark connector

StarRocks provides a self-developed connector named StarRocks Connector for Apache Spark™ (Spark connector for short) to help you read data from a StarRocks table by using Spark. You can use Spark for complex processing and machine learning on the data you have read from StarRocks.

The Spark connector supports three reading methods: Spark SQL, Spark DataFrame, and Spark RDD.

You can use Spark SQL to create a temporary view on the StarRocks table, and then directly read data from the StarRocks table by using that temporary view.

You can also map the StarRocks table to a Spark DataFrame or a Spark RDD, and then read data from the Spark DataFrame or Spark RDD. We recommend the use of a Spark DataFrame.

Usage notes

• Currently, you can only read data from StarRocks. You cannot write data from a sink to StarRocks.
• You can filter data on StarRocks before you read the data, thereby reducing the amount of data transferred.
• If the overhead of reading data is substantial, you can employ appropriate table design and filter conditions to prevent Spark from reading an excessive amount of data at a time. As such, you can reduce I/O pressure on your disk and network connection, thereby ensuring routine queries can be run properly.

Version requirements

Prerequisites

Spark has been deployed.

Before you begin

You can directly download the compiled Spark Connector Jar file. You can also compile Spark connector by yourself.

Download the compiled Jar file

• Spark 2.x
• Spark 3.x

Compile by yourself

1. Download the Spark connector package.

2. Take one of the following actions to compile the Spark connector:

   • If you are using Spark v2.x, run the following command, which compiles the Spark connector to suit Spark v2.3.4 by default:

     sh build.sh 2

   • If you are using Spark v3.x, run the following command, which compiles the Spark connector to suit Spark v3.1.2 by default:

     sh build.sh 3

3. Go to the output/ path to find the starrocks-spark2_2.11-1.0.0.jar file generated upon compilation. Then, copy the file to the classpath of Spark:

   • If your Spark cluster runs in Local mode, place the file into the jars/ path.
   • If your Spark cluster runs in Yarn mode, place the file into the pre-deployment package.

You can use the Spark connector to read data from StarRocks only after you place the file into the specified location.

Parameters

This section describes the parameters you need to configure when you use the Spark connector to read data from StarRocks.

Common parameters

The following parameters apply to all three reading methods: Spark SQL, Spark DataFrame, and Spark RDD.

Parameters for Spark SQL and Spark DataFrame

The following parameters apply only to the Spark SQL and Spark DataFrame reading methods.

Parameters for Spark RDD

The following parameters apply only to the Spark RDD reading method.

Data type mapping between StarRocks and Spark

The processing logic of the underlying storage engine used by StarRocks cannot cover an expected time range when DATE and DATETIME data types are directly used. Therefore, the Spark connector maps the DATE and DATETIME data types from StarRocks to the STRING data type from Spark, and generates readable string texts matching the date and time data read from StarRocks.

Examples

The following examples assume you have created a database named test in your StarRocks cluster and you have the permissions of user root.

Data example

Do as follows to prepare a sample table:

1. Go to the test database and create a table named score_board.

   MySQL [test]> CREATE TABLE `score_board`
   (
       `id` int(11) NOT NULL COMMENT "",
       `name` varchar(65533) NULL DEFAULT "" COMMENT "",
       `score` int(11) NOT NULL DEFAULT "0" COMMENT ""
   )
   ENGINE=OLAP
   PRIMARY KEY(`id`)
   COMMENT "OLAP"
   DISTRIBUTED BY HASH(`id`) BUCKETS 1
   PROPERTIES (
       "replication_num" = "3"
   );

2. Insert data into the score_board table.

   MySQL [test]> INSERT INTO score_board
   VALUES
       (1, 'Bob', 21),
       (2, 'Stan', 21),
       (3, 'Sam', 22),
       (4, 'Tony', 22),
       (5, 'Alice', 22),
       (6, 'Lucy', 23),
       (7, 'Polly', 23),
       (8, 'Tom', 23),
       (9, 'Rose', 24),
       (10, 'Jerry', 24),
       (11, 'Jason', 24),
       (12, 'Lily', 25),
       (13, 'Stephen', 25),
       (14, 'David', 25),
       (15, 'Eddie', 26),
       (16, 'Kate', 27),
       (17, 'Cathy', 27),
       (18, 'Judy', 27),
       (19, 'Julia', 28),
       (20, 'Robert', 28),
       (21, 'Jack', 29);

3. Query the score_board table.

   MySQL [test]> SELECT * FROM score_board;
   +------+---------+-------+
   | id   | name    | score |
   +------+---------+-------+
   |    1 | Bob     |    21 |
   |    2 | Stan    |    21 |
   |    3 | Sam     |    22 |
   |    4 | Tony    |    22 |
   |    5 | Alice   |    22 |
   |    6 | Lucy    |    23 |
   |    7 | Polly   |    23 |
   |    8 | Tom     |    23 |
   |    9 | Rose    |    24 |
   |   10 | Jerry   |    24 |
   |   11 | Jason   |    24 |
   |   12 | Lily    |    25 |
   |   13 | Stephen |    25 |
   |   14 | David   |    25 |
   |   15 | Eddie   |    26 |
   |   16 | Kate    |    27 |
   |   17 | Cathy   |    27 |
   |   18 | Judy    |    27 |
   |   19 | Julia   |    28 |
   |   20 | Robert  |    28 |
   |   21 | Jack    |    29 |
   +------+---------+-------+
   21 rows in set (0.01 sec)

Read data using Spark SQL

1. Run the following command in the Spark directory to start Spark SQL:

   sh spark-sql

2. Run the following command to create a temporary view named spark_starrocks on the score_board table which belongs to the test database:

   spark-sql> CREATE TEMPORARY VIEW spark_starrocks
              USING starrocks
              OPTIONS
              (
                  "starrocks.table.identifier" = "test.score_board",
                  "starrocks.fenodes" = "<fe_host>:<fe_http_port>",
                  "user" = "root",
                  "password" = ""
              );

3. Run the following command to read data from the temporary view:

   spark-sql> SELECT * FROM spark_starrocks;

   Spark returns the following data:

   1        Bob        21
   2        Stan        21
   3        Sam        22
   4        Tony        22
   5        Alice        22
   6        Lucy        23
   7        Polly        23
   8        Tom        23
   9        Rose        24
   10        Jerry        24
   11        Jason        24
   12        Lily        25
   13        Stephen        25
   14        David        25
   15        Eddie        26
   16        Kate        27
   17        Cathy        27
   18        Judy        27
   19        Julia        28
   20        Robert        28
   21        Jack        29
   Time taken: 1.883 seconds, Fetched 21 row(s)
   22/08/09 15:29:36 INFO thriftserver.SparkSQLCLIDriver: Time taken: 1.883 seconds, Fetched 21 row(s)

Read data using Spark DataFrame

1. Run the following command in the Spark directory to start Spark Shell:

   sh spark-shell

2. Run the following command to create a DataFrame named starrocksSparkDF on the score_board table which belongs to the test database:

   scala> val starrocksSparkDF = spark.read.format("starrocks")
              .option("starrocks.table.identifier", s"test.score_board")
              .option("starrocks.fenodes", s"<fe_host>:<fe_http_port>")
              .option("user", s"root")
              .option("password", s"")
              .load()

3. Read data from the DataFrame. For example, if you want to read the first 10 rows, run the following command:

   scala> starrocksSparkDF.show(10)

   Spark returns the following data:

   +---+-----+-----+
   | id| name|score|
   +---+-----+-----+
   |  1|  Bob|   21|
   |  2| Stan|   21|
   |  3|  Sam|   22|
   |  4| Tony|   22|
   |  5|Alice|   22|
   |  6| Lucy|   23|
   |  7|Polly|   23|
   |  8|  Tom|   23|
   |  9| Rose|   24|
   | 10|Jerry|   24|
   +---+-----+-----+
   only showing top 10 rows

   NOTE

   By default, if you do not specify the number of rows you want to read, Spark returns the first 20 rows.

Read data using Spark RDD

1. Run the following command in the Spark directory to start Spark Shell:

   sh spark-shell

2. Run the following command to create an RDD named starrocksSparkRDD on the score_board table which belongs to the test database.

   scala> import com.starrocks.connector.spark._
   scala> val starrocksSparkRDD = sc.starrocksRDD
              (
              tableIdentifier = Some("test.score_board"),
              cfg = Some(Map(
                  "starrocks.fenodes" -> "<fe_host>:<fe_http_port>",
                  "starrocks.request.auth.user" -> "root",
                  "starrocks.request.auth.password" -> ""
              ))
              )

3. Read data from the RDD. For example, if you want to read the first 10 elements, run the following command:

   scala> starrocksSparkRDD.take(10)

   Spark returns the following data:

   res0: Array[AnyRef] = Array([1, Bob, 21], [2, Stan, 21], [3, Sam, 22], [4, Tony, 22], [5, Alice, 22], [6, Lucy, 23], [7, Polly, 23], [8, Tom, 23], [9, Rose, 24], [10, Jerry, 24])

   To read the entire RDD, run the following command:

   scala> starrocksSparkRDD.collect()

   Spark returns the following data:

   res1: Array[AnyRef] = Array([1, Bob, 21], [2, Stan, 21], [3, Sam, 22], [4, Tony, 22], [5, Alice, 22], [6, Lucy, 23], [7, Polly, 23], [8, Tom, 23], [9, Rose, 24], [10, Jerry, 24], [11, Jason, 24], [12, Lily, 25], [13, Stephen, 25], [14, David, 25], [15, Eddie, 26], [16, Kate, 27], [17, Cathy, 27], [18, Judy, 27], [19, Julia, 28], [20, Robert, 28], [21, Jack, 29])

Best practices

When you read data from StarRocks using the Spark connector, you can use the starrocks.filter.query parameter to specify filter conditions based on which Spark prunes partitions, buckets, and prefix indexes to reduce the cost of data pulling. This section uses Spark DataFrame as an example to show how this is achieved.

Environment setup

Data example

Do as follows to prepare a sample table:

1. Go to the test database and create a table named mytable.

   MySQL [test]> CREATE TABLE `mytable`
   (
       `k` int(11) NULL COMMENT "bucket",
       `b` int(11) NULL COMMENT "",
       `dt` datetime NULL COMMENT "",
       `v` int(11) NULL COMMENT ""
   )
   ENGINE=OLAP
   DUPLICATE KEY(`k`,`b`, `dt`)
   COMMENT "OLAP"
   PARTITION BY RANGE(`dt`)
   (
       PARTITION p202201 VALUES [('2022-01-01 00:00:00'), ('2022-02-01 00:00:00')),
       PARTITION p202202 VALUES [('2022-02-01 00:00:00'), ('2022-03-01 00:00:00')),
       PARTITION p202203 VALUES [('2022-03-01 00:00:00'), ('2022-04-01 00:00:00'))
   )
   DISTRIBUTED BY HASH(`k`) BUCKETS 3
   PROPERTIES (
       "replication_num" = "3"
   );

2. Insert data into mytable.

   MySQL [test]> INSERT INTO mytable
   VALUES
        (1, 11, '2022-01-02 08:00:00', 111),
        (2, 22, '2022-02-02 08:00:00', 222),
        (3, 33, '2022-03-02 08:00:00', 333);

3. Query the mytable table.

   MySQL [test]> select * from mytable;
   +------+------+---------------------+------+
   | k    | b    | dt                  | v    |
   +------+------+---------------------+------+
   |    1 |   11 | 2022-01-02 08:00:00 |  111 |
   |    2 |   22 | 2022-02-02 08:00:00 |  222 |
   |    3 |   33 | 2022-03-02 08:00:00 |  333 |
   +------+------+---------------------+------+
   3 rows in set (0.01 sec)

Full table scan

1. Run the following command in the Spark directory to create a DataFrame named df on mytable table which belongs to the test database:

   scala>  val df = spark.read.format("starrocks")
           .option("starrocks.table.identifier", s"test.mytable")
           .option("starrocks.fenodes", s"<fe_host>:<fe_http_port>")
           .option("user", s"root")
           .option("password", s"")
           .load()

2. View the FE log file fe.log of your StarRocks cluster, and find the SQL statement executed to read data. Example:

   2022-08-09 18:57:38,091 INFO (nioEventLoopGroup-3-10|196) [TableQueryPlanAction.executeWithoutPassword():126] receive SQL statement [select `k`,`b`,`dt`,`v` from `test`.`mytable`] from external service [ user ['root'@'%']] for database [test] table [mytable]

3. In the test database, use EXPLAIN to obtain the execution plan of the SELECT k,b,dt,v from test.mytable statement:

   MySQL [test]> EXPLAIN select `k`,`b`,`dt`,`v` from `test`.`mytable`;
   +-----------------------------------------------------------------------+
   | Explain String                                                        |
   +-----------------------------------------------------------------------+
   | PLAN FRAGMENT 0                                                       |
   |  OUTPUT EXPRS:1: k | 2: b | 3: dt | 4: v                              |
   |   PARTITION: UNPARTITIONED                                            |
   |                                                                       |
   |   RESULT SINK                                                         |
   |                                                                       |
   |   1:EXCHANGE                                                          |
   |                                                                       |
   | PLAN FRAGMENT 1                                                       |
   |  OUTPUT EXPRS:                                                        |
   |   PARTITION: RANDOM                                                   |
   |                                                                       |
   |   STREAM DATA SINK                                                    |
   |     EXCHANGE ID: 01                                                   |
   |     UNPARTITIONED                                                     |
   |                                                                       |
   |   0:OlapScanNode                                                      |
   |      TABLE: mytable                                                   |
   |      PREAGGREGATION: ON                                               |
   |      partitions=3/3                                                   |
   |      rollup: mytable                                                  |
   |      tabletRatio=9/9                                                  |
   |      tabletList=41297,41299,41301,41303,41305,41307,41309,41311,41313 |
   |      cardinality=3                                                    |
   |      avgRowSize=4.0                                                   |
   |      numNodes=0                                                       |
   +-----------------------------------------------------------------------+
   26 rows in set (0.00 sec)

In this example, no pruning is performed. Therefore, Spark scans all of the three partitions (as suggested by partitions=3/3) that hold data, and scans all of the 9 tablets (as suggested by tabletRatio=9/9) in those three partitions.

Partition pruning

1. Run the following command, in which you use the starrocks.filter.query parameter to specify a filter condition dt='2022-01-02 08:00:00 for partition pruning, in the Spark directory to create a DataFrame named df on the mytable table which belongs to the test database:

   scala> val df = spark.read.format("starrocks")
          .option("starrocks.table.identifier", s"test.mytable")
          .option("starrocks.fenodes", s"<fe_host>:<fe_http_port>")
          .option("user", s"root")
          .option("password", s"")
          .option("starrocks.filter.query", "dt='2022-01-02 08:00:00'")
          .load()

2. View the FE log file fe.log of your StarRocks cluster, and find the SQL statement executed to read data. Example:

   2022-08-09 19:02:31,253 INFO (nioEventLoopGroup-3-14|204) [TableQueryPlanAction.executeWithoutPassword():126] receive SQL statement [select `k`,`b`,`dt`,`v` from `test`.`mytable` where dt='2022-01-02 08:00:00'] from external service [ user ['root'@'%']] for database [test] table [mytable]

3. In the test database, use EXPLAIN to obtain the execution plan of the SELECT k,b,dt,v from test.mytable where dt='2022-01-02 08:00:00' statement:

   MySQL [test]> EXPLAIN select `k`,`b`,`dt`,`v` from `test`.`mytable` where dt='2022-01-02 08:00:00';
   +------------------------------------------------+
   | Explain String                                 |
   +------------------------------------------------+
   | PLAN FRAGMENT 0                                |
   |  OUTPUT EXPRS:1: k | 2: b | 3: dt | 4: v       |
   |   PARTITION: UNPARTITIONED                     |
   |                                                |
   |   RESULT SINK                                  |
   |                                                |
   |   1:EXCHANGE                                   |
   |                                                |
   | PLAN FRAGMENT 1                                |
   |  OUTPUT EXPRS:                                 |
   |   PARTITION: RANDOM                            |
   |                                                |
   |   STREAM DATA SINK                             |
   |     EXCHANGE ID: 01                            |
   |     UNPARTITIONED                              |
   |                                                |
   |   0:OlapScanNode                               |
   |      TABLE: mytable                            |
   |      PREAGGREGATION: ON                        |
   |      PREDICATES: 3: dt = '2022-01-02 08:00:00' |
   |      partitions=1/3                            |
   |      rollup: mytable                           |
   |      tabletRatio=3/3                           |
   |      tabletList=41297,41299,41301              |
   |      cardinality=1                             |
   |      avgRowSize=20.0                           |
   |      numNodes=0                                |
   +------------------------------------------------+
   27 rows in set (0.01 sec)

In this example, only partition pruning is performed, whereas bucket pruning is not. Therefore, Spark scans one of the three partitions (as suggested by partitions=1/3) and all of the tablets (as suggested by tabletRatio=3/3) in that partition.

Bucket pruning

1. Run the following command, in which you use the starrocks.filter.query parameter to specify a filter condition k=1 for bucket pruning, in the Spark directory to create a DataFrame named df on the mytable table which belongs to the test database:

   scala> val df = spark.read.format("starrocks")
          .option("starrocks.table.identifier", s"test.mytable")
          .option("starrocks.fenodes", s"<fe_host>:<fe_http_port>")
          .option("user", s"root")
          .option("password", s"")
          .option("starrocks.filter.query", "k=1")
          .load()

2. View the FE log file fe.log of your StarRocks cluster, and find the SQL statement executed to read data. Example:

   2022-08-09 19:04:44,479 INFO (nioEventLoopGroup-3-16|208) [TableQueryPlanAction.executeWithoutPassword():126] receive SQL statement [select `k`,`b`,`dt`,`v` from `test`.`mytable` where k=1] from external service [ user ['root'@'%']] for database [test] table [mytable]

3. In the test database, use EXPLAIN to obtain the execution plan of the SELECT k,b,dt,v from test.mytable where k=1 statement:

   MySQL [test]> EXPLAIN select `k`,`b`,`dt`,`v` from `test`.`mytable` where k=1;
   +------------------------------------------+
   | Explain String                           |
   +------------------------------------------+
   | PLAN FRAGMENT 0                          |
   |  OUTPUT EXPRS:1: k | 2: b | 3: dt | 4: v |
   |   PARTITION: UNPARTITIONED               |
   |                                          |
   |   RESULT SINK                            |
   |                                          |
   |   1:EXCHANGE                             |
   |                                          |
   | PLAN FRAGMENT 1                          |
   |  OUTPUT EXPRS:                           |
   |   PARTITION: RANDOM                      |
   |                                          |
   |   STREAM DATA SINK                       |
   |     EXCHANGE ID: 01                      |
   |     UNPARTITIONED                        |
   |                                          |
   |   0:OlapScanNode                         |
   |      TABLE: mytable                      |
   |      PREAGGREGATION: ON                  |
   |      PREDICATES: 1: k = 1                |
   |      partitions=3/3                      |
   |      rollup: mytable                     |
   |      tabletRatio=3/9                     |
   |      tabletList=41299,41305,41311        |
   |      cardinality=1                       |
   |      avgRowSize=20.0                     |
   |      numNodes=0                          |
   +------------------------------------------+
   27 rows in set (0.01 sec)

In this example, only bucket pruning is performed, whereas partition pruning is not. Therefore, Spark scans all of the three partitions (as suggested by partitions=3/3) that hold data, and scans all of the three tablets (as suggested by tabletRatio=3/9) to retrieve Hash values that meet the k = 1 filter condition within those three partitions.

Partition pruning and bucket pruning

1. Run the following command, in which you use the starrocks.filter.query parameter to specify two filter conditions k=7 and dt='2022-01-02 08:00:00' for bucket pruning and partition pruning, in the Spark directory to create a DataFrame named df on the mytable table on the test database:

   scala> val df = spark.read.format("starrocks")
          .option("starrocks.table.identifier", s"test.mytable")
          .option("starrocks.fenodes", s"<fe_host>:<fe_http_port>")
          .option("user", s"")
          .option("password", s"")
          .option("starrocks.filter.query", "k=7 and dt='2022-01-02 08:00:00'")
          .load()

2. View the FE log file fe.log of your StarRocks cluster, and find the SQL statement executed to read data. Example:

   2022-08-09 19:06:34,939 INFO (nioEventLoopGroup-3-18|212) [TableQueryPlanAction.executeWithoutPassword():126] receive SQL statement [select `k`,`b`,`dt`,`v` from `test`.`mytable` where k=7 and dt='2022-01-02 08:00:00'] from external service [ user ['root'@'%']] for database [test] t
   able [mytable]

3. In the test database, use EXPLAIN to obtain the execution plan of the SELECT k,b,dt,v from test.mytable where k=7 and dt='2022-01-02 08:00:00' statement:

   MySQL [test]> EXPLAIN select `k`,`b`,`dt`,`v` from `test`.`mytable` where k=7 and dt='2022-01-02 08:00:00';
   +----------------------------------------------------------+
   | Explain String                                           |
   +----------------------------------------------------------+
   | PLAN FRAGMENT 0                                          |
   |  OUTPUT EXPRS:1: k | 2: b | 3: dt | 4: v                 |
   |   PARTITION: RANDOM                                      |
   |                                                          |
   |   RESULT SINK                                            |
   |                                                          |
   |   0:OlapScanNode                                         |
   |      TABLE: mytable                                      |
   |      PREAGGREGATION: ON                                  |
   |      PREDICATES: 1: k = 7, 3: dt = '2022-01-02 08:00:00' |
   |      partitions=1/3                                      |
   |      rollup: mytable                                     |
   |      tabletRatio=1/3                                     |
   |      tabletList=41301                                    |
   |      cardinality=1                                       |
   |      avgRowSize=20.0                                     |
   |      numNodes=0                                          |
   +----------------------------------------------------------+
   17 rows in set (0.00 sec)

In this example, both partition pruning and bucket pruning are performed. Therefore, Spark scans only one of the three partitions (as suggested by partitions=1/3) and only one tablet (as suggested by tabletRatio=1/3) in that partition.

Prefix index filtering

1. Insert more data records into a partition of the mytable table which belongs to the test database:

   MySQL [test]> INSERT INTO mytable
   VALUES
       (1, 11, "2022-01-02 08:00:00", 111), 
       (3, 33, "2022-01-02 08:00:00", 333), 
       (3, 33, "2022-01-02 08:00:00", 333), 
       (3, 33, "2022-01-02 08:00:00", 333);

2. Query the mytable table:

   MySQL [test]> SELECT * FROM mytable;
   +------+------+---------------------+------+
   | k    | b    | dt                  | v    |
   +------+------+---------------------+------+
   |    1 |   11 | 2022-01-02 08:00:00 |  111 |
   |    1 |   11 | 2022-01-02 08:00:00 |  111 |
   |    3 |   33 | 2022-01-02 08:00:00 |  333 |
   |    3 |   33 | 2022-01-02 08:00:00 |  333 |
   |    3 |   33 | 2022-01-02 08:00:00 |  333 |
   |    2 |   22 | 2022-02-02 08:00:00 |  222 |
   |    3 |   33 | 2022-03-02 08:00:00 |  333 |
   +------+------+---------------------+------+
   7 rows in set (0.01 sec)

3. Run the following command, in which you use the starrocks.filter.query parameter to specify a filter condition k=1 for prefix index filtering, in the Spark directory to create a DataFrame named df on the mytable table which belongs to the test database:

   scala> val df = spark.read.format("starrocks")
          .option("starrocks.table.identifier", s"test.mytable")
          .option("starrocks.fenodes", s"<fe_host>:<fe_http_port>")
          .option("user", s"root")
          .option("password", s"")
          .option("starrocks.filter.query", "k=1")
          .load()

4. In the test database, set is_report_success to true to enable profile reporting:

   MySQL [test]> SET is_report_success = true;
   Query OK, 0 rows affected (0.00 sec)

5. Use a browser to open the http://<fe_host>:<http_http_port>/query page, and view the profile of the SELECT * FROM mytable where k=1 statement. Example:

   OLAP_SCAN (plan_node_id=0):
     CommonMetrics:
        - CloseTime: 1.255ms
        - OperatorTotalTime: 1.404ms
        - PeakMemoryUsage: 0.00 
        - PullChunkNum: 8
        - PullRowNum: 2
          - __MAX_OF_PullRowNum: 2
          - __MIN_OF_PullRowNum: 0
        - PullTotalTime: 148.60us
        - PushChunkNum: 0
        - PushRowNum: 0
        - PushTotalTime: 0ns
        - SetFinishedTime: 136ns
        - SetFinishingTime: 129ns
     UniqueMetrics:
        - Predicates: 1: k = 1
        - Rollup: mytable
        - Table: mytable
        - BytesRead: 88.00 B
          - __MAX_OF_BytesRead: 88.00 B
          - __MIN_OF_BytesRead: 0.00 
        - CachedPagesNum: 0
        - CompressedBytesRead: 844.00 B
          - __MAX_OF_CompressedBytesRead: 844.00 B
          - __MIN_OF_CompressedBytesRead: 0.00 
        - CreateSegmentIter: 18.582us
        - IOTime: 4.425us
        - LateMaterialize: 17.385us
        - PushdownPredicates: 3
        - RawRowsRead: 2
          - __MAX_OF_RawRowsRead: 2
          - __MIN_OF_RawRowsRead: 0
        - ReadPagesNum: 12
          - __MAX_OF_ReadPagesNum: 12
          - __MIN_OF_ReadPagesNum: 0
        - RowsRead: 2
          - __MAX_OF_RowsRead: 2
          - __MIN_OF_RowsRead: 0
        - ScanTime: 154.367us
        - SegmentInit: 95.903us
          - BitmapIndexFilter: 0ns
          - BitmapIndexFilterRows: 0
          - BloomFilterFilterRows: 0
          - ShortKeyFilterRows: 3
            - __MAX_OF_ShortKeyFilterRows: 3
            - __MIN_OF_ShortKeyFilterRows: 0
          - ZoneMapIndexFilterRows: 0
        - SegmentRead: 2.559us
          - BlockFetch: 2.187us
          - BlockFetchCount: 2
            - __MAX_OF_BlockFetchCount: 2
            - __MIN_OF_BlockFetchCount: 0
          - BlockSeek: 7.789us
          - BlockSeekCount: 2
            - __MAX_OF_BlockSeekCount: 2
            - __MIN_OF_BlockSeekCount: 0
          - ChunkCopy: 25ns
          - DecompressT: 0ns
          - DelVecFilterRows: 0
          - IndexLoad: 0ns
          - PredFilter: 353ns
          - PredFilterRows: 0
          - RowsetsReadCount: 7
          - SegmentsReadCount: 3
            - __MAX_OF_SegmentsReadCount: 2
            - __MIN_OF_SegmentsReadCount: 0
          - TotalColumnsDataPageCount: 8
            - __MAX_OF_TotalColumnsDataPageCount: 8
            - __MIN_OF_TotalColumnsDataPageCount: 0
        - UncompressedBytesRead: 508.00 B
          - __MAX_OF_UncompressedBytesRead: 508.00 B
          - __MIN_OF_UncompressedBytesRead: 0.00 

In this example, the filter condition k = 1 can hit the prefix index. Therefore, Spark can filter out three rows (as suggested by ShortKeyFilterRows: 3).