STREAMING_TIMELINE 视图

INFORMATION_SCHEMA.STREAMING_TIMELINE 视图包含当前项目的每分钟汇总流式统计信息。

您可以查询 INFORMATION_SCHEMA 流式视图以检索有关使用旧版 tabledata.insertAll 方法(而非 BigQuery Storage Write API)将数据流式插入到 BigQuery 的历史信息和实时信息。如需详细了解如何将数据流式插入 BigQuery,请参阅将数据流式插入 BigQuery

所需权限

要查询 INFORMATION_SCHEMA.STREAMING_TIMELINE 视图,您需要拥有项目的 bigquery.tables.list Identity and Access Management (IAM) 权限。

以下每个预定义的 IAM 角色均可提供所需的权限:

  • roles/bigquery.user
  • roles/bigquery.dataViewer
  • roles/bigquery.dataEditor
  • roles/bigquery.dataOwner
  • roles/bigquery.metadataViewer
  • roles/bigquery.resourceAdmin
  • roles/bigquery.admin

如需详细了解 BigQuery 权限,请参阅使用 IAM 进行访问权限控制

架构

查询 INFORMATION_SCHEMA 流式视图时,查询结果包含有关将数据流式插入 BigQuery 的历史信息和实时信息。以下视图中的每一行均表示流式插入特定表的统计信息,这些信息从 start_timestamp 开始以一分钟为间隔进行了汇总。统计信息按错误代码分组,因此每个时间戳和表的组合在一分钟间隔内遇到的每个错误代码会占一行。如果请求成功,则错误代码设置为 NULL。如果在特定时间段内没有数据流式插入到表中,则该表对应的时间戳没有行。

INFORMATION_SCHEMA.STREAMING_TIMELINE_BY_* 视图具有以下架构:

列名 数据类型
start_timestamp TIMESTAMP (分区列)汇总统计信息的 1 分钟间隔的开始时间戳。
project_id STRING (聚簇列)项目的 ID。
project_number INTEGER 项目编号。
dataset_id STRING (聚簇列)数据集的 ID。
table_id STRING (聚簇列)表的 ID。
error_code STRING 为此行指定的请求返回的错误代码。如果请求成功,则错误代码为 NULL。
total_requests INTEGER 1 分钟间隔内的请求总数。
total_rows INTEGER 1 分钟间隔内所有请求的总行数。
total_input_bytes INTEGER 1 分钟间隔内所有行的总字节数。

数据保留

此视图包含过去 180 天的流式插入历史记录。

范围和语法

针对此视图的查询必须包含区域限定符。如果您未指定区域限定符,则会从所有区域检索元数据。下表说明了此视图的区域范围:

视图名称 资源范围 区域范围
[PROJECT_ID.]`region-REGION`.INFORMATION_SCHEMA.STREAMING_TIMELINE[_BY_PROJECT] 项目级 REGION
替换以下内容:

  • 可选:PROJECT_ID:您的 Google Cloud 项目的 ID。如果未指定,则使用默认项目。
  • REGION:任何数据集区域名称。例如 region-us

示例

  • 如需查询位于美国多区域的数据,请使用 `region-us`.INFORMATION_SCHEMA.STREAMING_TIMELINE_BY_PROJECT
  • 如需查询位于欧盟多区域的数据,请使用 `region-eu`.INFORMATION_SCHEMA.STREAMING_TIMELINE_BY_PROJECT
  • 如需查询位于 asia-northeast1 区域的数据,请使用 `region-asia-northeast1`.INFORMATION_SCHEMA.STREAMING_TIMELINE_BY_PROJECT

如需查看可用区域的列表,请参阅数据集位置

示例

示例 1:最近的流式插入失败

以下示例计算最近 30 分钟内项目中所有表的失败请求总数的每分钟明细,按错误代码划分:

SELECT
  start_timestamp,
  error_code,
  SUM(total_requests) AS num_failed_requests
FROM
  `region-us`.INFORMATION_SCHEMA.STREAMING_TIMELINE_BY_PROJECT
WHERE
  error_code IS NOT NULL
  AND start_timestamp > TIMESTAMP_SUB(CURRENT_TIMESTAMP, INTERVAL 30 MINUTE)
GROUP BY
  start_timestamp,
  error_code
ORDER BY
  start_timestamp DESC;

结果类似于以下内容:

+---------------------+------------------+---------------------+
|   start_timestamp   |    error_code    | num_failed_requests |
+---------------------+------------------+---------------------+
| 2020-04-15 20:55:00 | INTERNAL_ERROR   |                  41 |
| 2020-04-15 20:41:00 | CONNECTION_ERROR |                   5 |
| 2020-04-15 20:30:00 | INTERNAL_ERROR   |                 115 |
+---------------------+------------------+---------------------+
示例 2:所有产生错误代码的请求的每分钟明细

以下示例计算成功和失败的流式请求的每分钟明细,按错误代码划分。此查询可用于填充信息中心。

SELECT
  start_timestamp,
  SUM(total_requests) AS total_requests,
  SUM(total_rows) AS total_rows,
  SUM(total_input_bytes) AS total_input_bytes,
  SUM(
    IF(
      error_code IN ('QUOTA_EXCEEDED', 'RATE_LIMIT_EXCEEDED'),
      total_requests,
      0)) AS quota_error,
  SUM(
    IF(
      error_code IN (
        'INVALID_VALUE', 'NOT_FOUND', 'SCHEMA_INCOMPATIBLE',
        'BILLING_NOT_ENABLED', 'ACCESS_DENIED', 'UNAUTHENTICATED'),
      total_requests,
      0)) AS user_error,
  SUM(
    IF(
      error_code IN ('CONNECTION_ERROR','INTERNAL_ERROR'),
      total_requests,
      0)) AS server_error,
  SUM(IF(error_code IS NULL, 0, total_requests)) AS total_error,
FROM
  `region-us`.INFORMATION_SCHEMA.STREAMING_TIMELINE_BY_PROJECT
GROUP BY
  start_timestamp
ORDER BY
  start_timestamp DESC;

结果类似于以下内容:

+---------------------+----------------+------------+-------------------+-------------+------------+--------------+-------------+
|   start_timestamp   | total_requests | total_rows | total_input_bytes | quota_error | user_error | server_error | total_error |
+---------------------+----------------+------------+-------------------+-------------+------------+--------------+-------------+
| 2020-04-15 22:00:00 |         441854 |     441854 |       23784853118 |           0 |          0 |           17 |          17 |
| 2020-04-15 21:59:00 |         355627 |     355627 |       26101982742 |           0 |          0 |            0 |           0 |
| 2020-04-15 21:58:00 |         354603 |     354603 |       26160565341 |           0 |          0 |            0 |           0 |
| 2020-04-15 21:57:00 |         298823 |     298823 |       23877821442 |           0 |          0 |            0 |           0 |
+---------------------+----------------+------------+-------------------+-------------+------------+--------------+-------------+
示例 3:传入流量最多的表

以下示例返回传入流量最多的前 10 个表的流式统计信息:

SELECT
  project_id,
  dataset_id,
  table_id,
  SUM(total_rows) AS num_rows,
  SUM(total_input_bytes) AS num_bytes,
  SUM(total_requests) AS num_requests
FROM
  `region-us`.INFORMATION_SCHEMA.STREAMING_TIMELINE_BY_PROJECT
GROUP BY
  project_id,
  dataset_id,
  table_id
ORDER BY
  num_bytes DESC
LIMIT 10;

结果类似于以下内容:

+----------------------+------------+-------------------------------+------------+----------------+--------------+
|      project_id      | dataset_id |           table_id            |  num_rows  |   num_bytes    | num_requests |
+----------------------+------------+-------------------------------+------------+----------------+--------------+
| my-project           | dataset1   | table1                        | 8016725532 | 73787301876979 |   8016725532 |
| my-project           | dataset1   | table2                        |   26319580 | 34199853725409 |     26319580 |
| my-project           | dataset2   | table1                        |   38355294 | 22879180658120 |     38355294 |
| my-project           | dataset1   | table3                        |  270126906 | 17594235226765 |    270126906 |
| my-project           | dataset2   | table2                        |   95511309 | 17376036299631 |     95511309 |
| my-project           | dataset2   | table3                        |   46500443 | 12834920497777 |     46500443 |
| my-project           | dataset2   | table4                        |   25846270 |  7487917957360 |     25846270 |
| my-project           | dataset1   | table4                        |   18318404 |  5665113765882 |     18318404 |
| my-project           | dataset1   | table5                        |   42829431 |  5343969665771 |     42829431 |
| my-project           | dataset1   | table6                        |    8771021 |  5119004622353 |      8771021 |
+----------------------+------------+-------------------------------+------------+----------------+--------------+
示例 4:表的流式错误率

以下示例计算某个特定表的错误的每日明细,按错误代码划分:

SELECT
  TIMESTAMP_TRUNC(start_timestamp, DAY) as day,
  project_id,
  dataset_id,
  table_id,
  error_code,
  SUM(total_rows) AS num_rows,
  SUM(total_input_bytes) AS num_bytes,
  SUM(total_requests) AS num_requests
FROM
  `region-us`.INFORMATION_SCHEMA.STREAMING_TIMELINE_BY_PROJECT
WHERE
  table_id LIKE 'my_table'
GROUP BY
  project_id, dataset_id, table_id, error_code, day
ORDER BY
  day, project_id, dataset_id DESC;

结果类似于以下内容:

+---------------------+-------------+------------+----------+----------------+----------+-----------+--------------+
|         day         |  project_id | dataset_id | table_id |   error_code   | num_rows | num_bytes | num_requests |
+---------------------+-------------+------------+----------+----------------+----------+-----------+--------------+
| 2020-04-21 00:00:00 | my_project  | my_dataset | my_table | NULL           |       41 |    252893 |           41 |
| 2020-04-20 00:00:00 | my_project  | my_dataset | my_table | NULL           |     2798 |  10688286 |         2798 |
| 2020-04-19 00:00:00 | my_project  | my_dataset | my_table | NULL           |     2005 |   7979495 |         2005 |
| 2020-04-18 00:00:00 | my_project  | my_dataset | my_table | NULL           |     2054 |   7972378 |         2054 |
| 2020-04-17 00:00:00 | my_project  | my_dataset | my_table | NULL           |     2056 |   6978079 |         2056 |
| 2020-04-17 00:00:00 | my_project  | my_dataset | my_table | INTERNAL_ERROR |        4 |     10825 |            4 |
+---------------------+-------------+------------+----------+----------------+----------+-----------+--------------+