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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。
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 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。
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
本文示例采用 Docker Compose 部署，组件及版本号如下：
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
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)
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'}
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 < '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。
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)
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。
另外我们也创建了数据湖/湖仓一体专项支持群，有需求的伙伴可扫描下方二维码申请加入（备注：湖仓一体），即可获得专职工程师的技术指导与支持。
