clickhouse的MYsql物化视图 物化视图 clickhouse_java

最后更新: 2020-08-31

在 ClickHouse 里，物化视图(Materialized View)可以说是一个神奇且强大的东西，用途别具一格。

本文从底层机制进行分析，看看 ClickHouse 的 Materalized View 是怎么工作的，以方便更好的使用它。

什么是物化视图

对大部分人来说，物化视图这个概念会比较抽象，物化？视图？。。。

为了更好的理解它，我们先看一个场景。

假设你是 *hub 一个“幸福”的小程序员，某天产品经理有个需求：实时统计每小时视频下载量。

用户下载明细表：

clickhouse> SELECT * FROM download LIMIT 10;
+---------------------+--------+--------+
| when                | userid | bytes  |
+---------------------+--------+--------+
| 2020-08-31 18:22:06 |     19 | 530314 |
| 2020-08-31 18:22:06 |     19 | 872957 |
| 2020-08-31 18:22:06 |     19 | 107047 |
| 2020-08-31 18:22:07 |     19 | 214876 |
| 2020-08-31 18:22:07 |     19 | 820943 |
| 2020-08-31 18:22:07 |     19 | 693959 |
| 2020-08-31 18:22:08 |     19 | 882151 |
| 2020-08-31 18:22:08 |     19 | 644223 |
| 2020-08-31 18:22:08 |     19 | 199800 |
| 2020-08-31 18:22:09 |     19 | 511439 |

... ....

计算每小时下载量：

clickhouse> SELECT toStartOfHour(when) AS hour, userid, count() as downloads, sum(bytes) AS bytes FROM download GROUP BY userid, hour ORDER BY userid, hour;
+---------------------+--------+-----------+------------+
| hour                | userid | downloads | bytes      |
+---------------------+--------+-----------+------------+
| 2020-08-31 18:00:00 |     19 |      6822 | 3378623036 |
| 2020-08-31 19:00:00 |     19 |     10800 | 5424173178 |
| 2020-08-31 20:00:00 |     19 |     10800 | 5418656068 |
| 2020-08-31 21:00:00 |     19 |     10800 | 5404309443 |
| 2020-08-31 22:00:00 |     19 |     10800 | 5354077456 |
| 2020-08-31 23:00:00 |     19 |     10800 | 5390852563 |
| 2020-09-01 00:00:00 |     19 |     10800 | 5369839540 |
| 2020-09-01 01:00:00 |     19 |     10800 | 5384161012 |
| 2020-09-01 02:00:00 |     19 |     10800 | 5404581759 |
| 2020-09-01 03:00:00 |     19 |      6778 | 3399557322 |
+---------------------+--------+-----------+------------+
10 rows in set (0.13 sec)

很容易嘛，不过有个问题：每次都要以 download 表为基础数据进行计算，*hub 数据量太大，无法忍受。

想到一个办法：如果对 download 进行预聚合，把结果保存到一个新表 download_hour_mv，并随着 download 增量实时更新，每次去查询download_hour_mv 不就可以了。

这个新表可以看做是一个物化视图，它在 ClickHouse 是一个普通表。

创建物化视图

clickhouse> CREATE MATERIALIZED VIEW download_hour_mv
ENGINE = SummingMergeTree
PARTITION BY toYYYYMM(hour) ORDER BY (userid, hour)
AS SELECT
  toStartOfHour(when) AS hour,
  userid,
  count() as downloads,
  sum(bytes) AS bytes
FROM download WHERE when >= toDateTime('2020-09-01 04:00:00')
GROUP BY userid, hour

这个语句主要做了：

  • 创建一个引擎为 SummingMergeTree 的物化视图 download_hour_mv
  • 物化视图的数据来源于 download 表，并根据 select 语句中的表达式进行相应“物化”操作
  • 选取一个未来时间(当前时间是 2020-08-31 18:00:00)作为开始点 WHERE when >= toDateTime('2020-09-01 04:00:00')，表示在2020-09-01 04:00:00 之后的数据才会被同步到 download_hour_mv

这样，目前 download_hour_mv 是一个空表：

clickhouse> SELECT * FROM download_hour_mv ORDER BY userid, hour;
Empty set (0.02 sec)

注意：官方有 POPULATE(https://clickhouse.tech/docs/en/sql-reference/statements/create/view/#materialized) 关键字，但是不建议使用，因为视图创建期间 download 如果有写入数据会丢失，这也是我们加一个 WHERE 作为数据同步点的原因。

那么，我们如何让源表数据可以一致性的同步到 download_hour_mv 呢？

物化全量数据

在2020-09-01 04:00:00之后，我们可以通过一个带 WHERE 快照的INSERT INTO SELECT... 对 download 历史数据进行物化：

clickhouse> INSERT INTO download_hour_mv
SELECT
  toStartOfHour(when) AS hour,
  userid,
  count() as downloads,
  sum(bytes) AS bytes
FROM download WHERE when < toDateTime('2020-09-01 04:00:00')
GROUP BY userid, hour

查询物化视图：

clickhouse> SELECT * FROM download_hour_mv ORDER BY hour, userid, downloads DESC;
+---------------------+--------+-----------+------------+
| hour                | userid | downloads | bytes      |
+---------------------+--------+-----------+------------+
| 2020-08-31 18:00:00 |     19 |      6822 | 3378623036 |
| 2020-08-31 19:00:00 |     19 |     10800 | 5424173178 |
| 2020-08-31 20:00:00 |     19 |     10800 | 5418656068 |
| 2020-08-31 21:00:00 |     19 |     10800 | 5404309443 |
| 2020-08-31 22:00:00 |     19 |     10800 | 5354077456 |
| 2020-08-31 23:00:00 |     19 |     10800 | 5390852563 |
| 2020-09-01 00:00:00 |     19 |     10800 | 5369839540 |
| 2020-09-01 01:00:00 |     19 |     10800 | 5384161012 |
| 2020-09-01 02:00:00 |     19 |     10800 | 5404581759 |
| 2020-09-01 03:00:00 |     19 |      6778 | 3399557322 |
+---------------------+--------+-----------+------------+
10 rows in set (0.05 sec)

可以看到数据已经“物化”到 download_hour_mv。

物化增量数据

写一些数据到 download表:

clickhouse> INSERT INTO download
       SELECT
         toDateTime('2020-09-01 04:00:00') + number*(1/3) as when,
         19,
         rand() % 1000000
       FROM system.numbers
       LIMIT 10;

查询物化视图 download_hour_mv:

clickhouse> SELECT * FROM download_hour_mv ORDER BY hour, userid, downloads;
+---------------------+--------+-----------+------------+
| hour                | userid | downloads | bytes      |
+---------------------+--------+-----------+------------+
| 2020-08-31 18:00:00 |     19 |      6822 | 3378623036 |
| 2020-08-31 19:00:00 |     19 |     10800 | 5424173178 |
| 2020-08-31 20:00:00 |     19 |     10800 | 5418656068 |
| 2020-08-31 21:00:00 |     19 |     10800 | 5404309443 |
| 2020-08-31 22:00:00 |     19 |     10800 | 5354077456 |
| 2020-08-31 23:00:00 |     19 |     10800 | 5390852563 |
| 2020-09-01 00:00:00 |     19 |     10800 | 5369839540 |
| 2020-09-01 01:00:00 |     19 |     10800 | 5384161012 |
| 2020-09-01 02:00:00 |     19 |     10800 | 5404581759 |
| 2020-09-01 03:00:00 |     19 |      6778 | 3399557322 |
| 2020-09-01 04:00:00 |     19 |        10 |    5732600 |
+---------------------+--------+-----------+------------+
11 rows in set (0.00 sec)

可以看到最后一条数据就是我们增量的一个物化聚合，已经实时同步，这是如何做到的呢？

物化视图原理

ClickHouse 的物化视图原理并不复杂，在 download 表有新的数据写入时，如果检测到有物化视图跟它关联，会针对这批写入的数据进行物化操作。

比如上面新增数据是通过以下 SQL 生成的：

clickhouse> SELECT
    ->          toDateTime('2020-09-01 04:00:00') + number*(1/3) as when,
    ->          19,
    ->          rand() % 1000000
    ->        FROM system.numbers
    ->        LIMIT 10;
+---------------------+------+-------------------------+
| when                | 19   | modulo(rand(), 1000000) |
+---------------------+------+-------------------------+
| 2020-09-01 04:00:00 |   19 |                  870495 |
| 2020-09-01 04:00:00 |   19 |                  322270 |
| 2020-09-01 04:00:00 |   19 |                  983422 |
| 2020-09-01 04:00:01 |   19 |                  759708 |
| 2020-09-01 04:00:01 |   19 |                  975636 |
| 2020-09-01 04:00:01 |   19 |                  365507 |
| 2020-09-01 04:00:02 |   19 |                  865569 |
| 2020-09-01 04:00:02 |   19 |                  975742 |
| 2020-09-01 04:00:02 |   19 |                   85827 |
| 2020-09-01 04:00:03 |   19 |                  992779 |
+---------------------+------+-------------------------+
10 rows in set (0.02 sec)

物化视图执行的语句类似：

INSERT INTO download_hour_mv
SELECT
  toStartOfHour(when) AS hour,
  userid,
  count() as downloads,
  sum(bytes) AS bytes
FROM [新增的10条数据] WHERE when >= toDateTime('2020-09-01 04:00:00')
GROUP BY userid, hour

代码导航：

  1. 添加视图 OutputStream， InterpreterInsertQuery.cpp (https://github.com/ClickHouse/ClickHouse/blob/cb4644ea6d04b3d5900868b4f8d686a03082379a/src/Interpreters/InterpreterInsertQuery.cpp#L313)
if (table->noPushingToViews() && !no_destination)
                out = table->write(query_ptr, metadata_snapshot, context);
            else
                out = std::make_shared<PushingToViewsBlockOutputStream>(table, metadata_snapshot, context, query_ptr, no_destination);
  1. 构造 Insert ， PushingToViewsBlockOutputStream.cpp (https://github.com/ClickHouse/ClickHouse/blob/cb4644ea6d04b3d5900868b4f8d686a03082379a/src/DataStreams/PushingToViewsBlockOutputStream.cpp#L85)
ASTPtr insert_query_ptr(insert.release());
            InterpreterInsertQuery interpreter(insert_query_ptr, *insert_context);
            BlockIO io = interpreter.execute();
            out = io.out;
  1. 物化新增数据：PushingToViewsBlockOutputStream.cpp (https://github.com/ClickHouse/ClickHouse/blob/cb4644ea6d04b3d5900868b4f8d686a03082379a/src/DataStreams/PushingToViewsBlockOutputStream.cpp#L331)
Context local_context = *select_context;
            local_context.addViewSource(
                StorageValues::create(
                    storage->getStorageID(), metadata_snapshot->getColumns(), block, storage->getVirtuals()));
            select.emplace(view.query, local_context, SelectQueryOptions());
            in = std::make_shared<MaterializingBlockInputStream>(select->execute().getInputStream()

总结

物化视图的用途较多。

比如可以解决表索引问题，我们可以用物化视图创建另外一种物理序，来满足某些条件下的查询问题。

还有就是通过物化视图的实时同步数据能力，我们可以做到更加灵活的表结构变更。

更强大的地方是它可以借助 MergeTree 家族引擎(SummingMergeTree、Aggregatingmergetree等)，得到一个实时的预聚合，满足快速查询。

原理是把增量的数据根据 AS SELECT ... 对其进行处理并写入到物化视图表，物化视图是一种普通表，可以直接读取和写入。

全文完。

Enjoy ClickHouse :)