Sample SQL queries for Trace

Preview

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This document contains sample queries that are specific to querying trace data that is stored in your Google Cloud project.

SQL language support

Queries used in the Log Analytics page support GoogleSQL functions with some exceptions.

The following SQL commands aren't supported for SQL queries issued by using the Log Analytics page:

• DDL and DML commands
• Javascript user-defined functions
• BigQuery ML functions
• SQL variables

The following are supported only when you query a linked dataset by using the BigQuery Studio and Looker Studio pages, or by using the bq command-line tool:

• Javascript user-defined functions
• BigQuery ML functions
• SQL variables

Best practices

To set the time range of your query, we recommend that you use the time-range selector. For example, to view the data for the past week, select Last 7 days from the time-range selector. You can also use the time-range selector to specify a start and end time, specify a time to view around, and change time zones.

If you include a start_time field in the WHERE clause, then the time-range selector setting isn't used. The following example filters the data by using the TIMESTAMP_SUB function, which lets you specify a look-back interval from the current time:

WHERE
  start_time > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 HOUR)

For more information about how to filter by time, see Time functions and Timestamp functions.

Before you begin

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Observability API.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

Enable the API

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Observability API.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

Enable the API

1. To get the permissions that you need to load the Log Analytics page, write, run, and save private queries on your trace data, ask your administrator to grant you the following IAM roles:

   • Observability View Accessor (roles/observability.viewAccessor) on the observability views that you want to query. This role supports IAM conditions, which let you restrict the grant to a specific view. If you don't attach a condition to the role grant, then the principal can access all observability views.
   • Observability Analytics User (roles/observability.analyticsUser) on your project. This role contains the permissions required to save and run private queries, and to run shared queries.

   For more information about granting roles, see Manage access to projects, folders, and organizations.

   You might also be able to get the required permissions through custom roles or other predefined roles.

How to use the queries on this page

1. In the Google Cloud console, go to the Log Analytics page:

   Go to Log Analytics

   If you use the search bar to find this page, then select the result whose subheading is Logging.

2. In the Query pane, click the  SQL, and then copy and paste a query into the SQL query pane.

   Before you copy a query, in the FROM clause, replace PROJECT_ID with the ID of your Google Cloud project:

   FROM `PROJECT_ID.us._Trace.Spans._AllSpans`
   

   The fields us._Trace.Spans._AllSpans identify the location, the observability bucket, the dataset, and the view.

   Note: The delimiters for the path are period characters, (.). If a path element like the Google Cloud project contains a period character, then ensure every path field is wrapped by single backquotes, (`).

To use the queries shown in this document on the BigQuery Studio page or to use the bq command-line tool, then edit the FROM clause and enter the path to the linked dataset. For example, to query the _AllSpans view on the linked dataset named my_linked_dataset that is in the project myproject, the path is `myproject.my_linked_dataset._AllSpans`.

Common use cases

This section lists several common use cases, that might help you create your custom queries.

Show all trace data

To query the _AllSpans view, run the following query:

-- Display all data.
SELECT *
FROM `PROJECT_ID.us._Trace.Spans._AllSpans`
-- Limit to 10 entries.
LIMIT 10

Show common span information

To show common span information, like the start time and duration, run the run the following query:

SELECT
  start_time,
  -- Set the value of service name based on the first non-null value in the list.
  COALESCE(
    JSON_VALUE(resource.attributes, '$."service.name"'),
    JSON_VALUE(attributes, '$."service.name"'),
    JSON_VALUE(attributes, '$."g.co/gae/app/module"')) AS service_name,
  name AS span_name,
  duration_nano,
  status.code AS status,
  trace_id,
  span_id
FROM
  `PROJECT_ID.us._Trace.Spans._AllSpans`
LIMIT 10

To learn more, see Conditional expressions.

Show 50th and 99th percentiles of the span latency

To show the 50th and 99th percentiles of the latency for each rpc service, run the following query:

SELECT
  -- Compute 50th and 99th percentiles for each service
  STRING(attributes['rpc.service']) || '/' || STRING(attributes['rpc.method']) AS rpc_service_method,
  APPROX_QUANTILES(duration_nano, 100)[OFFSET(50)] AS duration_nano_p50,
  APPROX_QUANTILES(duration_nano, 100)[OFFSET(99)] AS duration_nano_p99
FROM
  `PROJECT_ID.us._Trace.Spans._AllSpans`
WHERE
  -- Matches spans whose kind field has a value of 2 (SPAN_KIND_SERVER).
  kind = 2
GROUP BY rpc_service_method

For more information about the enumeration, see the OpenTelemetry: SpanKind documentation.

To view the results graphically, you might create a chart with the dimension set to rpc_service_method. You might add two measures, one for the average of the duration_nano_p50 value, and the other for the average of the duration_nano_p99 field.

Filter trace entries

To apply a filter to your query, add a WHERE clause. The syntax that you use in this clause depends on the data type of the field. This section provides several examples for different data types.

Filter by string data types

The field name is stored as a String.

• To analyze only those spans where name is specified, use the following clause:

  -- Matches spans that have a name field.
  WHERE name IS NOT NULL
  

• To analyse only those spans where name has the value "POST", use the following clase

  -- Matches spans whose name is POST.
  WHERE STRPOS(name, "POST") > 0
  

• To analyse only those spans where name contains the value "POST", use the LIKE operator along with wildcards:

  -- Matches spans whose name contains POST.
  WHERE name LIKE "%POST%"
  

Filter by integer data types

The field kind is an integer, which can take values between zero and five:

• To analyze only those spans where kind is specified, use the following clause:

  -- Matches spans that have field named kind.
  WHERE kind IS NOT NULL
  

• To analyze spans whose kind value is either one or two, use the following clase:

  -- Matches spans whose kind value is 1 or 2.
  WHERE kind IN (1, 2)
  

Filter by RECORD data types

Some fields in the trace schema have a data type of RECORD. These fields can either store one or more data structures, or they store repeated entries of the same data structure.

Filter by status or status code

The status field is an example of a field whose data type is RECORD. This field stores one data structure, with members labeled code and message.

• To only analyze spans when the status.code field has a value of 1, add the following clause:

  -- Matches spans that have a status.code field that has a value of 1.
  WHERE status.code = 1
  

  The status.code field is stored as an integer.

• To analyze spans where the status field isn't EMPTY, add the following clause:

  -- Matches spans that have status field. When the status field exists, it
  -- must contain a subfield named code.
  -- Don't compare status to NULL, because this field has a data type of RECORD.
  WHERE status.code IS NOT NULL
  

Filter by events or links

The events and links fields are stored with a data type of RECORD, but these are repeated fields.

• To match spans that have at least one event, use the following clause:

  -- Matches spans that have at least one event. Don't compare events to NULL.
  -- The events field has data type of RECORD and contains a repeated fields.
  WHERE ARRAY_LENGTH(events) > 0
  

• To match spans that have an event whose name field has the value of message, use the following clause:

  WHERE
    -- Exists is true when any event in the array has a name field with the
    -- value of message.
    EXISTS(
      SELECT 1
      FROM UNNEST(events) AS ev
      WHERE ev.name = 'message'
    )
  

Filter by JSON data types

The attributes field is of type JSON. Each individual attribute is a key-value pair.

• To analyze only those spans where attributes is specified, use the following clause:

  -- Matches spans where at least one attribute is specified.
  WHERE attributes IS NOT NULL
  

• To analyze only those spans where the attribute key named component has a value of "proxy", use the following clause:

  -- Matches spans that have an attribute named component with a value of proxy.
  WHERE attributes IS NOT NULL
        AND JSON_VALUE(attributes, '$.component') = 'proxy'
  

  You can also use a LIKE statement along with wildcards to perform a contains test:

  -- Matches spans that have an attribute named component whose value contains proxy.
  WHERE attributes IS NOT NULL
        AND JSON_VALUE(attributes, '$.component') LIKE '%proxy%'
  

Group and aggregate trace data

This section illustrates how you can group and aggregate spans. If you don't specify a grouping but do specify an aggregation, one result is printed because SQL treats all entries that satisfy the WHERE clause as one group.

Every SELECT expression must be included in the group fields or be aggregated.

Group spans by start time

To group data by start time, use the function TIMESTAMP_TRUNC, which truncates a timestamp to a specified granularity like HOUR:

SELECT
  -- Truncate the start time to the hour. Count the number of spans per group.
  TIMESTAMP_TRUNC(start_time, HOUR) AS hour,
  status.code AS code,
  COUNT(*) AS count
FROM
  `PROJECT_ID.us._Trace.Spans._AllSpans`
WHERE
  -- Matches spans shows start time is within the previous 12 hours.
  start_time > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 12 HOUR)
GROUP BY
  -- Group by hour and status code.
  hour, code
ORDER BY hour DESC

For more information, see TIMESTAMP_TRUNC documentation and Datetime functions.

Note: If you include a start_time field in the WHERE clause, then the time-range selector setting isn't used.

Count spans by status code

To display the count of spans with a specific status code, run the following query:

SELECT
  -- Count the number of spans for each status code.
  status.code,
  COUNT(*) AS count
FROM
  `PROJECT_ID.us._Trace.Spans._AllSpans`
WHERE status.code IS NOT NULL
GROUP BY status.code

If you replace status.code with kind, then the previous query reports the number of spans for each value of the kind enumeration. Similarly, if you replace status.code with name, then the query results lists the number of entries for each span name.

Compute the average duration of all spans

To display the average duration, after grouping span data by span name, run the following query:

SELECT
  -- Group by name, and then compute the average duration for each group.
  name,
  AVG(duration_nano) AS nanosecs,
FROM
  `PROJECT_ID.us._Trace.Spans._AllSpans`
GROUP BY name
ORDER BY nanosecs DESC

Compute average duration and percentiles per service name

The following query computes the span count and various statics for each service:

SELECT
  -- Set the service name by the first non-null value.
  COALESCE(
    JSON_VALUE(resource.attributes, '$."service.name"'),
    JSON_VALUE(attributes, '$."service.name"'),
    JSON_VALUE(attributes, '$."g.co/gae/app/module"')) AS service_name,

  -- Count the number spans for each service name. Also compute statistics.
  COUNT(*) AS span_count,
  AVG(duration_nano) AS avg_duration_nano,
  MIN(duration_nano) AS min_duration_nano,
  MAX(duration_nano) AS max_duration_nano,

  -- Calculate percentiles for duration
  APPROX_QUANTILES(duration_nano, 100)[OFFSET(50)] AS p50_duration_nano,
  APPROX_QUANTILES(duration_nano, 100)[OFFSET(95)] AS p95_duration_nano,
  APPROX_QUANTILES(duration_nano, 100)[OFFSET(99)] AS p99_duration_nano,

  -- Count the number of unique trace IDs. Also, collect up to 5 unique
  -- span names and status codes.
  COUNT(DISTINCT trace_id) AS distinct_trace_count,
  ARRAY_AGG(DISTINCT name IGNORE NULLS LIMIT 5) AS sample_span_names,
  ARRAY_AGG(DISTINCT status.code IGNORE NULLS LIMIT 5) AS sample_status_codes
FROM
  `PROJECT_ID.us._Trace.Spans._AllSpans`
GROUP BY service_name
ORDER BY span_count DESC

Cross-column search

This section describes two approaches that you can use to search multiple columns of the view that you are querying:

• Token-based searches: You specify the search location, a search query, and then use the SEARCH function. Because the SEARCH function has specific rules on how the data is searched, we recommend that you read the SEARCH documentation.

• Substring-based searches: You provide the search location, a string literal, and then use the function CONTAINS_SUBSTR. The system performs a case-insensitive test to determine whether the string literal exists in an expression. The CONTAINS_SUBSTR function returns TRUE when the string literal exists and FALSE otherwise. The search value must be a STRING literal, but not the literal NULL.

Query multiple views

Query statements scan one or more tables or expressions and return the computed result rows. For example, you can use query statements to merge the results of SELECT statements on different tables or datasets in a variety of ways and then select the columns from the combined data.

When you query multiple views, those views must reside in the same location. For example, if two views are located in the us-east1 location, then one query can query both views. You can also query two views that are located in the us multi-region. However, if a view's location is global, then that view can reside in any physical location. Therefore, joins between two views that have the location of global might fail.

Join trace and log data by using the trace ID

The following query joins log and trace data by using the span and trace IDs:

SELECT
  T.trace_id,
  T.span_id,
  T.name,
  T.start_time,
  T.duration_nano,
  L.log_name,
  L.severity,
  L.json_payload
FROM
  `PROJECT_ID.us._Trace.Spans._AllSpans` AS T
JOIN
  `PROJECT_ID.us._Default._AllLogs` AS L
ON
  -- Join log and trace data by both the span ID and trace ID.
  -- Don't join only on span ID, this field isn't globally unique.
  T.span_id = L.span_id
  -- A regular expression is required because the storage format of the trace ID
  -- differs between a log view and a trace view.
  AND T.trace_id = REGEXP_EXTRACT(L.trace, r'/([^/]+)$')
WHERE T.duration_nano > 1000000
LIMIT 10

The response of the query lists the trace and span ID, which let you query for them individually to gather more information. Additionally, the results list the severity of the log entry and the JSON payload.

What's next

• Save and share a SQL query.
• Chart SQL query results.
• Query a linked dataset in BigQuery.

For SQL reference documentation or other examples, see the following documents:

• Functions, operators, and conditionals.
• Search functions.
• Query syntax.
• Sample SQL queries for Cloud Logging.