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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。
- Apache Doris + Apache Hudi 快速搭建指南｜Lakehouse 使用手册（一）
- Apache Doris + Apache Paimon 快速搭建指南｜Lakehouse 使用手册（二）
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：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：
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：
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：
-- 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：
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。
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。
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
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)
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
mysql> SELECT * FROM iceberg.nyc.taxis where vendor_id = 2 and ts >= '2024-01-01' and ts EXPLAIN VERBOSE SELECT * FROM iceberg.nyc.taxis where vendor_id = 2 and ts >= '2024-01-01' and ts = '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") = 1704067200000000 ....
通过EXPLAIN VERBOSE语句的结果可知，vendor_id = 2 and ts >= '2024-01-01' and ts 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。
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
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
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
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。
