作为一种全新的开放式的数据管理架构,湖仓一体(Data Lakehouse)融合了数据仓库的高性能、实时性以及数据湖的低成本、灵活性等优势,帮助用户更加便捷地满足各种数据处理分析的需求,在企业的大数据体系中已经得到越来越多的应用。

在过去多个版本中,Apache Doris 持续加深与数据湖的融合,当前已演进出一套成熟的湖仓一体解决方案。

  • 自 0.15 版本起,Apache Doris 引入 Hive 和 Iceberg 外部表,尝试在 Apache Iceberg 之上探索与数据湖的能力结合。
  • 自 1.2 版本起,Apache Doris 正式引入 Multi-Catalog 功能,实现了多种数据源的自动元数据映射和数据访问、并对外部数据读取和查询执行等方面做了诸多性能优化,完全具备了构建极速易用 Lakehouse 架构的能力。
  • 在 2.1 版本中,Apache Doris 湖仓一体架构得到全面加强,不仅增强了主流数据湖格式(Hudi、Iceberg、Paimon 等)的读取和写入能力,还引入了多 SQL 方言兼容、可从原有系统无缝切换至 Apache Doris。在数据科学及大规模数据读取场景上,Doris 集成了 Arrow Flight 高速读取接口,使得数据传输效率实现 100 倍的提升。

使用 Doris 和 Iceberg 构建 Lakehouse

Apache Doris & Iceberg

Apache Iceberg 是一种开源、高性能、高可靠的数据湖表格式,可实现超大规模数据的分析与管理。它支持 Apache Doris 在内的多种主流查询引擎,兼容 HDFS 以及各种对象云存储,具备 ACID、Schema 演进、高级过滤、隐藏分区和分区布局演进等特性,可确保高性能查询以及数据的可靠性及一致性,其时间旅行和版本回滚功能也为数据管理带来较高的灵活性。

Apache Doris 对 Iceberg 多项核心特性提供了原生支持:

  • 支持 Hive Metastore、Hadoop、REST、Glue、Google Dataproc Metastore、DLF 等多种 Iceberg Catalog 类型。
  • 原生支持 Iceberg V1/V2 表格式,以及 Position Delete、Equality Delete 文件的读取。
  • 支持通过表函数查询 Iceberg 表快照历史。
  • 支持时间旅行(Time Travel)功能。
  • 原生支持 Iceberg 表引擎。可以通过 Apache Doris 直接创建、管理以及将数据写入到 Iceberg 表。支持完善的分区 Transform 函数,从而提供隐藏分区和分区布局演进等能力。

用户可以基于 Apache Doris + Apache Iceberg 快速构建高效的湖仓一体解决方案,以灵活应对实时数据分析与处理的各种需求:

  • 通过 Doris 高性能查询引擎对 Iceberg 表数据和其他数据源进行关联数据分析,构建统一的联邦数据分析平台
  • 通过 Doris 直接管理和构建 Iceberg 表,在 Doris 中完成对数据的清洗、加工并写入到 Iceberg 表,构建统一的湖仓数据处理平台
  • 通过 Iceberg 表引擎,将 Doris 数据共享给其他上下游系统做进一步处理,构建统一的开放数据存储平台

未来,Apache Iceberg 将作为 Apache Doris 的原生表引擎之一,提供更加完善的湖格式数据的分析、管理功能。Apache Doris 也将逐步支持包括 Update/Delete/Merge、写回时排序、增量数据读取、元数据管理等 Apache Iceberg 更多高级特性,共同构建统一、高性能、实时的湖仓平台。

关于更多说明,请参阅 Iceberg Catalog

使用指南

本文档主要讲解如何在 Docker 环境下快速搭建 Apache Doris + Apache Iceberg 测试 & 演示环境,并展示各功能的使用操作。

本文涉及所有脚本和代码可以从该地址获取:https://github.com/apache/doris/tree/master/samples/datalake/iceberg_and_paimon

01 环境准备

本文示例采用 Docker Compose 部署,组件及版本号如下:

组件名称版本
Apache Doris默认 2.1.5,可修改
Apache Iceberg1.4.3
MinIORELEASE.2024-04-29T09-56-05Z

02 环境部署

  1. 启动所有组件

    bash ./start_all.sh

  2. 启动后,可以使用如下脚本,登陆 Doris 命令行:

    1. -- login doris
    2. bash ./start_doris_client.sh

03 创建 Iceberg 表

首先登陆 Doris 命令行后,Doris 集群中已经创建了名为 Iceberg 的 Catalog(可通过 SHOW CATALOGS/SHOW CREATE CATALOG iceberg 查看)。以下为该 Catalog 的创建语句:

  1. -- 已创建,无需执行
  2. CREATE CATALOG `iceberg` PROPERTIES (
  3. "type" = "iceberg",
  4. "iceberg.catalog.type" = "rest",
  5. "warehouse" = "s3://warehouse/",
  6. "uri" = "http://rest:8181",
  7. "s3.access_key" = "admin",
  8. "s3.secret_key" = "password",
  9. "s3.endpoint" = "http://minio:9000"
  10. );

在 Iceberg Catalog 创建数据库和 Iceberg 表:

  1. mysql> SWITCH iceberg;
  2. Query OK, 0 rows affected (0.00 sec)
  3. mysql> CREATE DATABASE nyc;
  4. Query OK, 0 rows affected (0.12 sec)
  5. mysql> CREATE TABLE iceberg.nyc.taxis
  6. (
  7. vendor_id BIGINT,
  8. trip_id BIGINT,
  9. trip_distance FLOAT,
  10. fare_amount DOUBLE,
  11. store_and_fwd_flag STRING,
  12. ts DATETIME
  13. )
  14. PARTITION BY LIST (vendor_id, DAY(ts)) ()
  15. PROPERTIES (
  16. "compression-codec" = "zstd",
  17. "write-format" = "parquet"
  18. );
  19. Query OK, 0 rows affected (0.15 sec)

04 数据写入

向 Iceberg 表中插入数据:

  1. mysql> INSERT INTO iceberg.nyc.taxis
  2. VALUES
  3. (1, 1000371, 1.8, 15.32, 'N', '2024-01-01 9:15:23'),
  4. (2, 1000372, 2.5, 22.15, 'N', '2024-01-02 12:10:11'),
  5. (2, 1000373, 0.9, 9.01, 'N', '2024-01-01 3:25:15'),
  6. (1, 1000374, 8.4, 42.13, 'Y', '2024-01-03 7:12:33');
  7. Query OK, 4 rows affected (1.61 sec)
  8. {'status':'COMMITTED', 'txnId':'10085'}

通过 CREATE TABLE AS SELECT 来创建一张 Iceberg 表:

  1. mysql> CREATE TABLE iceberg.nyc.taxis2 AS SELECT * FROM iceberg.nyc.taxis;
  2. Query OK, 6 rows affected (0.25 sec)
  3. {'status':'COMMITTED', 'txnId':'10088'}

05 数据查询

  • 简单查询

    1. mysql> SELECT * FROM iceberg.nyc.taxis;
    2. +-----------+---------+---------------+-------------+--------------------+----------------------------+
    3. | vendor_id | trip_id | trip_distance | fare_amount | store_and_fwd_flag | ts |
    4. +-----------+---------+---------------+-------------+--------------------+----------------------------+
    5. | 1 | 1000374 | 8.4 | 42.13 | Y | 2024-01-03 07:12:33.000000 |
    6. | 1 | 1000371 | 1.8 | 15.32 | N | 2024-01-01 09:15:23.000000 |
    7. | 2 | 1000373 | 0.9 | 9.01 | N | 2024-01-01 03:25:15.000000 |
    8. | 2 | 1000372 | 2.5 | 22.15 | N | 2024-01-02 12:10:11.000000 |
    9. +-----------+---------+---------------+-------------+--------------------+----------------------------+
    10. 4 rows in set (0.37 sec)
    11. mysql> SELECT * FROM iceberg.nyc.taxis2;
    12. +-----------+---------+---------------+-------------+--------------------+----------------------------+
    13. | vendor_id | trip_id | trip_distance | fare_amount | store_and_fwd_flag | ts |
    14. +-----------+---------+---------------+-------------+--------------------+----------------------------+
    15. | 1 | 1000374 | 8.4 | 42.13 | Y | 2024-01-03 07:12:33.000000 |
    16. | 1 | 1000371 | 1.8 | 15.32 | N | 2024-01-01 09:15:23.000000 |
    17. | 2 | 1000373 | 0.9 | 9.01 | N | 2024-01-01 03:25:15.000000 |
    18. | 2 | 1000372 | 2.5 | 22.15 | N | 2024-01-02 12:10:11.000000 |
    19. +-----------+---------+---------------+-------------+--------------------+----------------------------+
    20. 4 rows in set (0.35 sec)
  • 分区剪裁

    1. mysql> SELECT * FROM iceberg.nyc.taxis where vendor_id = 2 and ts >= '2024-01-01' and ts < '2024-01-02';
    2. +-----------+---------+---------------+-------------+--------------------+----------------------------+
    3. | vendor_id | trip_id | trip_distance | fare_amount | store_and_fwd_flag | ts |
    4. +-----------+---------+---------------+-------------+--------------------+----------------------------+
    5. | 2 | 1000373 | 0.9 | 9.01 | N | 2024-01-01 03:25:15.000000 |
    6. +-----------+---------+---------------+-------------+--------------------+----------------------------+
    7. 1 row in set (0.06 sec)
    8. mysql> EXPLAIN VERBOSE SELECT * FROM iceberg.nyc.taxis where vendor_id = 2 and ts >= '2024-01-01' and ts < '2024-01-02';
    9. ....
    10. | 0:VICEBERG_SCAN_NODE(71)
    11. | table: taxis
    12. | predicates: (ts[#5] < '2024-01-02 00:00:00'), (vendor_id[#0] = 2), (ts[#5] >= '2024-01-01 00:00:00')
    13. | inputSplitNum=1, totalFileSize=3539, scanRanges=1
    14. | partition=1/0
    15. | backends:
    16. | 10002
    17. | s3://warehouse/wh/nyc/taxis/data/vendor_id=2/ts_day=2024-01-01/40e6ca404efa4a44-b888f23546d3a69c_5708e229-2f3d-4b68-a66b-44298a9d9815-0.zstd.parquet start: 0 length: 3539
    18. | cardinality=6, numNodes=1
    19. | pushdown agg=NONE
    20. | icebergPredicatePushdown=
    21. | ref(name="ts") < 1704153600000000
    22. | ref(name="vendor_id") == 2
    23. | ref(name="ts") >= 1704067200000000
    24. ....

    通过 EXPLAIN VERBOSE 语句的结果可知,vendor_id = 2 and ts >= '2024-01-01' and ts < '2024-01-02' 谓词条件,最终只命中一个分区(partition=1/0)。

    同时也可知,因为在建表时指定了分区 Transform 函数 DAY(ts),原始数据中的的值 2024-01-01 03:25:15.000000 会被转换成文件目录中的分区信息 ts_day=2024-01-01

06 Time Travel

我们先再次插入几行数据:

  1. INSERT INTO iceberg.nyc.taxis VALUES (1, 1000375, 8.8, 55.55, 'Y', '2024-01-01 8:10:22'), (3, 1000376, 7.4, 32.35, 'N', '2024-01-02 1:14:45');
  2. Query OK, 2 rows affected (0.17 sec)
  3. {'status':'COMMITTED', 'txnId':'10086'}
  4. mysql> SELECT * FROM iceberg.nyc.taxis;
  5. +-----------+---------+---------------+-------------+--------------------+----------------------------+
  6. | vendor_id | trip_id | trip_distance | fare_amount | store_and_fwd_flag | ts |
  7. +-----------+---------+---------------+-------------+--------------------+----------------------------+
  8. | 3 | 1000376 | 7.4 | 32.35 | N | 2024-01-02 01:14:45.000000 |
  9. | 2 | 1000372 | 2.5 | 22.15 | N | 2024-01-02 12:10:11.000000 |
  10. | 1 | 1000374 | 8.4 | 42.13 | Y | 2024-01-03 07:12:33.000000 |
  11. | 1 | 1000371 | 1.8 | 15.32 | N | 2024-01-01 09:15:23.000000 |
  12. | 1 | 1000375 | 8.8 | 55.55 | Y | 2024-01-01 08:10:22.000000 |
  13. | 2 | 1000373 | 0.9 | 9.01 | N | 2024-01-01 03:25:15.000000 |
  14. +-----------+---------+---------------+-------------+--------------------+----------------------------+
  15. 6 rows in set (0.11 sec)

使用 iceberg_meta 表函数查询表的快照信息:

  1. mysql> select * from iceberg_meta("table" = "iceberg.nyc.taxis", "query_type" = "snapshots");
  2. +---------------------+---------------------+---------------------+-----------+-----------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
  3. | committed_at | snapshot_id | parent_id | operation | manifest_list | summary |
  4. +---------------------+---------------------+---------------------+-----------+-----------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
  5. | 2024-07-29 03:38:22 | 8483933166442433486 | -1 | append | s3://warehouse/wh/nyc/taxis/metadata/snap-8483933166442433486-1-5f7b7736-8022-4ba1-9db2-51ae7553be4d.avro | {"added-data-files":"4","added-records":"4","added-files-size":"14156","changed-partition-count":"4","total-records":"4","total-files-size":"14156","total-data-files":"4","total-delete-files":"0","total-position-deletes":"0","total-equality-deletes":"0"} |
  6. | 2024-07-29 03:40:23 | 4726331391239920914 | 8483933166442433486 | append | s3://warehouse/wh/nyc/taxis/metadata/snap-4726331391239920914-1-6aa3d142-6c9c-4553-9c04-08ad4d49a4ea.avro | {"added-data-files":"2","added-records":"2","added-files-size":"7078","changed-partition-count":"2","total-records":"6","total-files-size":"21234","total-data-files":"6","total-delete-files":"0","total-position-deletes":"0","total-equality-deletes":"0"} |
  7. +---------------------+---------------------+---------------------+-----------+-----------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
  8. 2 rows in set (0.07 sec)

使用 FOR VERSION AS OF 语句查询指定快照:

  1. mysql> SELECT * FROM iceberg.nyc.taxis FOR VERSION AS OF 8483933166442433486;
  2. +-----------+---------+---------------+-------------+--------------------+----------------------------+
  3. | vendor_id | trip_id | trip_distance | fare_amount | store_and_fwd_flag | ts |
  4. +-----------+---------+---------------+-------------+--------------------+----------------------------+
  5. | 1 | 1000371 | 1.8 | 15.32 | N | 2024-01-01 09:15:23.000000 |
  6. | 1 | 1000374 | 8.4 | 42.13 | Y | 2024-01-03 07:12:33.000000 |
  7. | 2 | 1000372 | 2.5 | 22.15 | N | 2024-01-02 12:10:11.000000 |
  8. | 2 | 1000373 | 0.9 | 9.01 | N | 2024-01-01 03:25:15.000000 |
  9. +-----------+---------+---------------+-------------+--------------------+----------------------------+
  10. 4 rows in set (0.05 sec)
  11. mysql> SELECT * FROM iceberg.nyc.taxis FOR VERSION AS OF 4726331391239920914;
  12. +-----------+---------+---------------+-------------+--------------------+----------------------------+
  13. | vendor_id | trip_id | trip_distance | fare_amount | store_and_fwd_flag | ts |
  14. +-----------+---------+---------------+-------------+--------------------+----------------------------+
  15. | 1 | 1000374 | 8.4 | 42.13 | Y | 2024-01-03 07:12:33.000000 |
  16. | 1 | 1000375 | 8.8 | 55.55 | Y | 2024-01-01 08:10:22.000000 |
  17. | 3 | 1000376 | 7.4 | 32.35 | N | 2024-01-02 01:14:45.000000 |
  18. | 2 | 1000372 | 2.5 | 22.15 | N | 2024-01-02 12:10:11.000000 |
  19. | 2 | 1000373 | 0.9 | 9.01 | N | 2024-01-01 03:25:15.000000 |
  20. | 1 | 1000371 | 1.8 | 15.32 | N | 2024-01-01 09:15:23.000000 |
  21. +-----------+---------+---------------+-------------+--------------------+----------------------------+
  22. 6 rows in set (0.04 sec)

使用 FOR TIME AS OF 语句查询指定快照:

  1. mysql> SELECT * FROM iceberg.nyc.taxis FOR TIME AS OF "2024-07-29 03:38:23";
  2. +-----------+---------+---------------+-------------+--------------------+----------------------------+
  3. | vendor_id | trip_id | trip_distance | fare_amount | store_and_fwd_flag | ts |
  4. +-----------+---------+---------------+-------------+--------------------+----------------------------+
  5. | 1 | 1000374 | 8.4 | 42.13 | Y | 2024-01-03 07:12:33.000000 |
  6. | 1 | 1000371 | 1.8 | 15.32 | N | 2024-01-01 09:15:23.000000 |
  7. | 2 | 1000372 | 2.5 | 22.15 | N | 2024-01-02 12:10:11.000000 |
  8. | 2 | 1000373 | 0.9 | 9.01 | N | 2024-01-01 03:25:15.000000 |
  9. +-----------+---------+---------------+-------------+--------------------+----------------------------+
  10. 4 rows in set (0.04 sec)
  11. mysql> SELECT * FROM iceberg.nyc.taxis FOR TIME AS OF "2024-07-29 03:40:22";
  12. +-----------+---------+---------------+-------------+--------------------+----------------------------+
  13. | vendor_id | trip_id | trip_distance | fare_amount | store_and_fwd_flag | ts |
  14. +-----------+---------+---------------+-------------+--------------------+----------------------------+
  15. | 2 | 1000373 | 0.9 | 9.01 | N | 2024-01-01 03:25:15.000000 |
  16. | 1 | 1000374 | 8.4 | 42.13 | Y | 2024-01-03 07:12:33.000000 |
  17. | 2 | 1000372 | 2.5 | 22.15 | N | 2024-01-02 12:10:11.000000 |
  18. | 1 | 1000371 | 1.8 | 15.32 | N | 2024-01-01 09:15:23.000000 |
  19. +-----------+---------+---------------+-------------+--------------------+----------------------------+
  20. 4 rows in set (0.05 sec)