Apache Doris + Iceberg 快速搭建指南｜Lakehouse 使用手册（三）

SelectDB2024-08-02 16:46

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，能够更加便捷地满足各种数据处理分析的需求。Apache Doris 持续加深与数据湖的融合，已演进出一套成熟的湖仓一体解决方案。我们将通过一系列文章介绍 Apache Doris 与各类主流数据湖格式及存储系统的湖仓一体架构搭建指南，包括 Hudi、Paimon、Iceberg、OSS、Delta Lake、Kudu、BigQuery 等。

本文将继续为大家介绍 Lakehouse 使用手册（三）之 Apache Doris + Apache Iceberg 快速搭建指南。

Apache Doris + Apache 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。

接下来，为读者讲解如何在 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。

使用指南

本文涉及脚本&代码从该地址获取：github.com/apache/dori...

01 环境准备

本文示例采用 Docker Compose 部署，组件及版本号如下：

​

​

02 环境部署

1. 启动所有组件

bash 复制代码 
bash ./start_all.sh

2. 启动后，可以使用如下脚本，登陆 Doris 命令行：

bash 复制代码 
bash ./start_doris_client.sh

03 创建 Iceberg 表

1. 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：

ini 复制代码 
-- 已创建，无需执行
CREATE CATALOG `iceberg` PROPERTIES (
    "type" = "iceberg",
    "iceberg.catalog.type" = "rest",
    "warehouse" = "s3://warehouse/",
    "uri" = "http://rest:8181",
    "s3.access_key" = "admin",
    "s3.secret_key" = "password",
    "s3.endpoint" = "http://minio:9000"
);

2. 在 Iceberg Catalog 创建数据库和 Iceberg 表：

sql 复制代码 
  mysql> SWITCH iceberg;
  Query OK, 0 rows affected (0.00 sec)
​
  mysql> CREATE DATABASE nyc;
  Query OK, 0 rows affected (0.12 sec)
​
  mysql> CREATE TABLE iceberg.nyc.taxis
         (
             vendor_id BIGINT,
             trip_id BIGINT,
             trip_distance FLOAT,
             fare_amount DOUBLE,
             store_and_fwd_flag STRING,
             ts DATETIME
         )
         PARTITION BY LIST (vendor_id, DAY(ts)) ()
         PROPERTIES (
             "compression-codec" = "zstd",
             "write-format" = "parquet"
         );
  Query OK, 0 rows affected (0.15 sec)

04 数据写入

1. 向 Iceberg 表中插入数据。

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

2. 通过 CREATE TABLE AS SELECT 来创建一张 Iceberg 表。

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

05 数据查询

  • 简单查询
sql 复制代码 
  mysql> SELECT * FROM iceberg.nyc.taxis;
  +-----------+---------+---------------+-------------+--------------------+----------------------------+
  | vendor_id | trip_id | trip_distance | fare_amount | store_and_fwd_flag | ts                         |
  +-----------+---------+---------------+-------------+--------------------+----------------------------+
  |         1 | 1000374 |           8.4 |       42.13 | Y                  | 2024-01-03 07:12:33.000000 |
  |         1 | 1000371 |           1.8 |       15.32 | N                  | 2024-01-01 09:15:23.000000 |
  |         2 | 1000373 |           0.9 |        9.01 | N                  | 2024-01-01 03:25:15.000000 |
  |         2 | 1000372 |           2.5 |       22.15 | N                  | 2024-01-02 12:10:11.000000 |
  +-----------+---------+---------------+-------------+--------------------+----------------------------+
  4 rows in set (0.37 sec)
sql 复制代码 
  mysql> SELECT * FROM iceberg.nyc.taxis2;
  +-----------+---------+---------------+-------------+--------------------+----------------------------+
  | vendor_id | trip_id | trip_distance | fare_amount | store_and_fwd_flag | ts                         |
  +-----------+---------+---------------+-------------+--------------------+----------------------------+
  |         1 | 1000374 |           8.4 |       42.13 | Y                  | 2024-01-03 07:12:33.000000 |
  |         1 | 1000371 |           1.8 |       15.32 | N                  | 2024-01-01 09:15:23.000000 |
  |         2 | 1000373 |           0.9 |        9.01 | N                  | 2024-01-01 03:25:15.000000 |
  |         2 | 1000372 |           2.5 |       22.15 | N                  | 2024-01-02 12:10:11.000000 |
  +-----------+---------+---------------+-------------+--------------------+----------------------------+
  4 rows in set (0.35 sec)
​

  • 分区剪裁

    ini 复制代码 
    mysql> SELECT * FROM iceberg.nyc.taxis where vendor_id = 2 and ts >= '2024-01-01' and ts < '2024-01-02';
+-----------+---------+---------------+-------------+--------------------+----------------------------+
| vendor_id | trip_id | trip_distance | fare_amount | store_and_fwd_flag | ts                         |
+-----------+---------+---------------+-------------+--------------------+----------------------------+
|         2 | 1000373 |           0.9 |        9.01 | N                  | 2024-01-01 03:25:15.000000 |
+-----------+---------+---------------+-------------+--------------------+----------------------------+
1 row in set (0.06 sec)
​
mysql> EXPLAIN VERBOSE SELECT * FROM iceberg.nyc.taxis where vendor_id = 2 and ts >= '2024-01-01' and ts < '2024-01-02';
​
....                                                                                                                                                                                  
|   0:VICEBERG_SCAN_NODE(71)                                                                                                                                                          
|      table: taxis                                                                                                                                                                   
|      predicates: (ts[#5] < '2024-01-02 00:00:00'), (vendor_id[#0] = 2), (ts[#5] >= '2024-01-01 00:00:00')                                                                           
|      inputSplitNum=1, totalFileSize=3539, scanRanges=1                                                                                                                              
|      partition=1/0                                                                                                                                                                  
|      backends:                                                                                                                                                                      
|        10002                                                                                                                                                                        
|          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 
|      cardinality=6, numNodes=1                                                                                                                                                      
|      pushdown agg=NONE                                                                                                                                                              
|      icebergPredicatePushdown=                                                                                                                                                      
|           ref(name="ts") < 1704153600000000                                                                                                                                         
|           ref(name="vendor_id") == 2                                                                                                                                                
|           ref(name="ts") >= 1704067200000000                                                                                                                                        
....

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

1. 再次插入几行数据。

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

2. 使用 iceberg_meta表函数查询表的快照信息

sql 复制代码 
mysql> select * from iceberg_meta("table" = "iceberg.nyc.taxis", "query_type" = "snapshots");
+---------------------+---------------------+---------------------+-----------+-----------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| committed_at        | snapshot_id         | parent_id           | operation | manifest_list                                                                                             | summary                                                                                                                                                                                                                                                        |
+---------------------+---------------------+---------------------+-----------+-----------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 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"} |
| 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"}  |
+---------------------+---------------------+---------------------+-----------+-----------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
2 rows in set (0.07 sec)

3. 使用 FOR VERSION AS OF语句查询指定快照

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

4. 使用 FOR TIME AS OF语句查询指定快照

sql 复制代码 
  mysql> SELECT * FROM iceberg.nyc.taxis FOR TIME AS OF "2024-07-29 03:38:23";
  +-----------+---------+---------------+-------------+--------------------+----------------------------+
  | vendor_id | trip_id | trip_distance | fare_amount | store_and_fwd_flag | ts                         |
  +-----------+---------+---------------+-------------+--------------------+----------------------------+
  |         1 | 1000374 |           8.4 |       42.13 | Y                  | 2024-01-03 07:12:33.000000 |
  |         1 | 1000371 |           1.8 |       15.32 | N                  | 2024-01-01 09:15:23.000000 |
  |         2 | 1000372 |           2.5 |       22.15 | N                  | 2024-01-02 12:10:11.000000 |
  |         2 | 1000373 |           0.9 |        9.01 | N                  | 2024-01-01 03:25:15.000000 |
  +-----------+---------+---------------+-------------+--------------------+----------------------------+
  4 rows in set (0.04 sec)
​
  mysql> SELECT * FROM iceberg.nyc.taxis FOR TIME AS OF "2024-07-29 03:40:22";
  +-----------+---------+---------------+-------------+--------------------+----------------------------+
  | vendor_id | trip_id | trip_distance | fare_amount | store_and_fwd_flag | ts                         |
  +-----------+---------+---------------+-------------+--------------------+----------------------------+
  |         2 | 1000373 |           0.9 |        9.01 | N                  | 2024-01-01 03:25:15.000000 |
  |         1 | 1000374 |           8.4 |       42.13 | Y                  | 2024-01-03 07:12:33.000000 |
  |         2 | 1000372 |           2.5 |       22.15 | N                  | 2024-01-02 12:10:11.000000 |
  |         1 | 1000371 |           1.8 |       15.32 | N                  | 2024-01-01 09:15:23.000000 |
  +-----------+---------+---------------+-------------+--------------------+----------------------------+
  4 rows in set (0.05 sec)

结束语

以上是基于 Apache Doris 与 Apache Iceberg 快速搭建测试 / 演示环境的详细指南，后续我们还将陆续推出 Apache Doris 与各类主流数据湖格式及存储系统构建湖仓一体架构的系列指南，欢迎持续关注。

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