Sample SQL queries

This document contains sample queries that you can use to query your log and trace data.

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.

Log data

If you include a timestamp field in the WHERE clause, then the time-range selector setting isn't used. The following example illustrates how to filter by timestamp:

-- Matches log entries whose timestamp is within the most recent 1 hour.
WHERE timestamp > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 HOUR)

Trace data

If you include a start_time field in the WHERE clause, then the time-range selector setting isn't used. The following example illustrates how to filter by timestamp:

-- Matches trace spans whose start_time is within the most recent 1 hour.
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 views are in Public Preview.
   • 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.
   • Logs View Accessor (roles/logging.viewAccessor) on the project that stores the log views that you want to query.

   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.

   Log data

   Before you copy a query, in the FROM clause, replace the following fields

   • PROJECT_ID: The identifier of the project.
   • LOCATION: The location of the log view or the analytics view.
   • BUCKET_ID: The name or ID of the log bucket.
   • LOG_VIEW_ID: The identifier of the log view, which is limited to 100 characters and can include only letters, digits, underscores, and hyphens.

   The following shows the format of the FROM clause for a log view:

   FROM `PROJECT_ID.LOCATION.BUCKET_ID.LOG_VIEW_ID`
   

   The log samples on this page query a log view. To query an analytics view, use the following path format: `analytics_view.PROJECT_ID.LOCATION.ANALYTICS_VIEW_ID`. In the previous expression, PROJECT_ID is the ID of your project, and LOCATION and ANALYTICS_VIEW_ID, are the location and name of your analytics view.

   Trace data

   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.

Log data

For example, to query the _AllLogs view on the linked dataset named mydataset that is in the project myproject, the path is myproject.mydataset._AllLogs.

Trace data

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 log entries in the default log bucket

To query the _Default bucket, run the following query:

SELECT
  timestamp, severity, resource.type, log_name, text_payload, proto_payload, json_payload
FROM
  `PROJECT_ID.LOCATION._Default._AllLogs`
-- Limit to 1000 entries
LIMIT 1000

Extract field value by regular expression

To extract a value from a string by using a regular expression, use the function REGEXP_EXTRACT:

SELECT
  -- Display the timestamp, and the part of the name that begins with test.
  timestamp, REGEXP_EXTRACT(JSON_VALUE(json_payload.jobName), r".*(test.*)$") AS name,
FROM
  `PROJECT_ID.LOCATION.BUCKET_ID.LOG_VIEW_ID`
WHERE
  -- Get the value of jobName, which is a subfield in a JSON structure.
  JSON_VALUE(json_payload.jobName) IS NOT NULL
ORDER BY timestamp DESC
LIMIT 20

For more information, see the REGEXP_EXTRACT documentation.

For substring matches, like the previous query, use of the CONTAINS_SUBSTR function results in a more efficient query.

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 log 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 log entries by payload type

Log entries can have one of three payload types. To filter log entries by the payload type, use one of the following clauses:

• Text payloads

  -- Matches log entries that have a text payload.
  WHERE text_payload IS NOT NULL
  

• JSON payloads

  -- Matches log entries that have a JSON payload.
  WHERE json_payload IS NOT NULL
  

• Proto payloads

  -- Matches log entries that have a proto payload.
  -- Because proto_payload has a data type of RECORD, this statement tests
  -- whether a mandatory subfield exits.
  WHERE proto_payload.type IS NOT NULL
  

In the query results, both the json_payload and proto_payload fields are rendered in JSON, which you can navigate through.

Filter log data by timestamp

To filter log entries by their timestamp, we recommend that you use the time-range selector. However, you can also specify the timestamp in the WHERE clause:

-- Matches log entries whose timestamp is within the most recent hour
WHERE timestamp > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 HOUR)

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

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

Filter by resource

To filter your log and trace data by resource, add a resource.type statement to the WHERE clause:

-- Matches log entries whose resource type is gce_instance
WHERE resource.type = "gce_instance"

Filter by severity

To filter your log data by a severity, add a severity statement to the WHERE clause:

-- Matches log entries whose severity is INFO or ERROR
WHERE severity IS NOT NULL AND severity IN ('INFO', 'ERROR')

You can also filter your log entries by the severity_number, which is an integer. For example, the following clause matches all log entries whose severity level is at least NOTICE:

-- Matches log entries whose severity level is at least NOTICE
WHERE severity_number IS NOT NULL AND severity_number > 200

For information about the enumerated values, see LogSeverity.

Filter by log name

To filter your log data by a log name, add a log_name or log_id statement to the WHERE clause:

• Log name specifies the resource path:

  -- Matches log entries that have the following log ID.
  WHERE log_name="projects/cloud-logs-test-project/logs/cloudaudit.googleapis.com%2Factivity"
  

• Log ID omits the resource path:

  -- Matches log entries that have the following log id.
  WHERE log_id = "cloudaudit.googleapis.com/data_access"
  

Filter log entries by resource label

Resource labels are stored as a JSON structure. To filter by the value of a field within a JSON structure, use the function JSON_VALUE:

SELECT
  timestamp, JSON_VALUE(resource.labels.zone) AS zone, json_payload, resource, labels
FROM
  `PROJECT_ID.LOCATION.BUCKET_ID.LOG_VIEW_ID`
WHERE
  -- Matches log entries whose resource type is gce_instance and whose zone is
  -- us-central1-f. Because resource has data type JSON, you must use JSON_VALUE
  -- to get the value for subfields, like zone.
  resource.type = "gce_instance" AND
  JSON_VALUE(resource.labels.zone) = "us-central1-f"
ORDER BY timestamp ASC

The previous query relies on the format of resource labels, as they are stored in a log entry. The following is an example of the resource field:

{
   type: "gce_instance"
   labels: {
      instance_id: "1234512345123451"
      project_id: "my-project"
      zone: "us-central1-f"
   }
}

For information about all functions that can retrieve and transform JSON data, see JSON functions.

Filter by HTTP request

To only query log entries that have an HTTP request field, use the following clause:

-- Matches log entries that have a HTTP request_method field.
-- Don't compare http_request to NULL. This field has a data type of RECORD.
WHERE http_request.request_method IS NOT NULL

You can also use the IN statement:

-- Matches log entries whose HTTP request_method is GET or POST.
WHERE http_request.request_method IN ('GET', 'POST')

Filter by HTTP status

To only query log entries that have an HTTP status, use the following clause:

-- Matches log entries that have an http_request.status field.
WHERE http_request.status IS NOT NULL

Filter by a field within a JSON data type

To only query log entries when the subfield of a field with a JSON data type has a specific value, extract the value by using the function JSON_VALUE:

-- Compare the value of the status field to NULL.
WHERE JSON_VALUE(json_payload.status) IS NOT NULL

The previous clause is subtly different than the following clause:

-- Compare the status field to NULL.
WHERE json_payload.status IS NOT NULL

The first clause tests whether the value of the status field is NULL. The second clause tests whether the status field exists. Suppose a log view contains two log entries. For one log entry, the json_payload field has the following form:

{
    status: {
        measureTime: "1661517845"
    }
}

For the other log entry, the json_payload field has a different structure:

{
    @type: "type.googleapis.com/google.cloud.scheduler.logging.AttemptFinished"
    jobName: "projects/my-project/locations/us-central1/jobs/test1"
    relativeUrl: "/food=cake"
    status: "NOT_FOUND"
    targetType: "APP_ENGINE_HTTP"
}

The clause WHERE json_payload.status IS NOT NULL matches both log entries. However, the clause WHERE JSON_VALUE(json_payload.status) IS NOT NULL only matches the second log entry.

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 log entries

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

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

Group log entries by timestamp

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

SELECT
  -- Truncate the timestamp by hour.
  TIMESTAMP_TRUNC(timestamp, HOUR) AS hour,
  JSON_VALUE(json_payload.status) AS status,
  -- Count the number log entries in each group.
  COUNT(*) AS count
FROM
  `PROJECT_ID.LOCATION.BUCKET_ID.LOG_VIEW_ID`
WHERE
  -- Matches log entries that have a status field whose value isn't NULL.
  json_payload IS NOT NULL AND JSON_VALUE(json_payload.status) IS NOT NULL
GROUP BY
  -- Group by hour and status
  hour,status
ORDER BY hour ASC

For more information, see TIMESTAMP_TRUNC documentation and Datetime functions.

Group log entries by resource

The following query shows how to group log entries by the resource type, and then count the number of log entries in each group:

SELECT
   -- Count the number of log entries for each resource type
   resource.type, COUNT(*) AS count
FROM
  `PROJECT_ID.LOCATION.BUCKET_ID.LOG_VIEW_ID`
GROUP BY resource.type
LIMIT 100

Group log entries by severity

The following query shows how to group log entries by the severity, and then count the number of log entries in each group:

SELECT
  -- Count the number of log entries for each severity.
  severity, COUNT(*) AS count
FROM
  `PROJECT_ID.LOCATION.BUCKET_ID.LOG_VIEW_ID`
WHERE
  severity IS NOT NULL
GROUP BY severity
ORDER BY severity
LIMIT 100

Group log entries by their log_id

The following query shows how to group log entries by the log ID, and then count the number of log entries in each group:

SELECT
  -- Count the number of log entries for each log ID.
  log_id, COUNT(*) AS count
FROM
  `PROJECT_ID.LOCATION.BUCKET_ID.LOG_VIEW_ID`
GROUP BY log_id
ORDER BY count DESC
LIMIT 100

Compute average latency of HTTP requests per URL

The following query illustrates how to group log entries by the HTTP request URL and location, and then count the number of log entries in each group:

SELECT
  -- Compute the average latency for each group. Because the labels field has a
  -- data type of JSON, use JSON_VALUE to get the value of checker_location.
  JSON_VALUE(labels.checker_location) AS location,
  AVG(http_request.latency.seconds) AS secs, http_request.request_url
FROM
  `PROJECT_ID.LOCATION.BUCKET_ID.LOG_VIEW_ID`
WHERE
  -- Matches log entries when the request_method field is GET.
  http_request IS NOT NULL AND http_request.request_method IN ('GET')
GROUP BY
  -- Group by request URL and location
  http_request.request_url, location
ORDER BY location
LIMIT 100

Compute average bytes sent for a subnetwork test

The following query shows how to group log entries by the location specified in the resource labels, and then compute the number of log entries in each group:

SELECT
  -- Compute the average number of bytes sent per location. Because labels has
  -- a data type of JSON, use JSON_VALUE to get the value of the location field.
  -- bytes_sent is a string. Must cast to a FLOAT64 before computing average.
  JSON_VALUE(resource.labels.location) AS location,
  AVG(CAST(JSON_VALUE(json_payload.bytes_sent) AS FLOAT64)) AS bytes
FROM
  `PROJECT_ID.LOCATION.BUCKET_ID.LOG_VIEW_ID`
WHERE
  resource.type = "gce_subnetwork" AND json_payload IS NOT NULL
GROUP BY
  -- Group by location
  location
LIMIT 100

For more information, see JSON functions and Conversion functions.

Count the log entries with a field that matches a pattern

To return the substring that matches a regular expression, use the function REGEXP_EXTRACT:

SELECT
  -- Extract the value that begins with test.
  -- Count the number of log entries for each name.
  REGEXP_EXTRACT(JSON_VALUE(json_payload.jobName), r".*(test.*)$") AS name,
  COUNT(*) AS count
FROM
  `PROJECT_ID.LOCATION.BUCKET_ID.LOG_VIEW_ID`
WHERE
  json_payload.jobName IS NOT NULL
GROUP BY name
ORDER BY count
LIMIT 20

For additional examples, see the REGEXP_EXTRACT documentation.

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.

Token-based search on a log view

The following query retains only those rows that have a field that exactly matches "35.193.12.15":

SELECT
  timestamp, log_id, proto_payload, severity, resource.type, resource, labels
FROM
  `PROJECT_ID.LOCATION.BUCKET_ID.LOG_VIEW_ID` AS t
WHERE
  -- Search data access audit logs for the IP address that matches 35.193.12.15.
  -- The use of backticks prevents the string from being tokenized.
  proto_payload IS NOT NULL AND
  log_id = "cloudaudit.googleapis.com/data_access" AND
  SEARCH(t,"`35.193.12.15`")
ORDER BY timestamp ASC
LIMIT 20

When backticks are omitted in the query string, the query string is split based on rules defined in the SEARCH documentation. For example, when the following statement is run, the query string is split into four tokens: "35", "193", "12", and "15":

  SEARCH(t,"35.193.12.15")

The previous SEARCH statement matches a row when a single field matches all four tokens. The order of the tokens doesn't matter.

You can include multiple SEARCH statements in a query. For example, in the previous query, you could replace the filter on the log ID with a statement like the following:

  SEARCH(t,"`cloudaudit.googleapis.com/data_access`")

The previous statement searches every field of the log entries in the log view while the original statement searches only the log_id field of the log entries.

To perform multiple searches on multiple fields, separate the individual strings with a space. For example, the following statement matches rows where a field contains "Hello World", "happy", and "days":

  SEARCH(t,"`Hello World` happy days")

Lastly, you can search specific fields instead of searching an entire table. For example, the following statement only searches the columns named text_payload and json_payload:

   SEARCH((text_payload, json_payload) ,"`35.222.132.245`")

For information about how the parameters of the SEARCH function are processed, see the BigQuery reference page Search functions.

Substring search on a log view

For example, the following query fetches all Data Access audit log entries with a specific IP address whose timestamps are in a specific time range. Lastly, the query sorts the results and then displays the 20 oldest results:

SELECT
  timestamp, log_id, proto_payload, severity, resource.type, resource, labels
FROM
  `PROJECT_ID.LOCATION.BUCKET_ID.LOG_VIEW_ID` AS t
WHERE
  -- Search data access audit logs for the IP address that matches 35.193.12.15.
  -- CONTAINS_SUBSTR performs a contains-test.
  proto_payload IS NOT NULL AND
  log_id = "cloudaudit.googleapis.com/data_access" AND
  CONTAINS_SUBSTR(t,"35.193.12.15")
ORDER BY timestamp ASC
LIMIT 20

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 two log views by the trace ID

To combine information from two tables, use one of the join operators:

SELECT
  -- Do an inner join on two tables by using the span ID and trace ID.
  -- Don't join only by span ID, as this field isn't globally unique.
  -- From the first view, show the timestamp, severity, and JSON payload.
  -- From the second view, show the JSON payload.
  a.timestamp, a.severity, a.json_payload, b.json_payload, a.span_id, a.trace
FROM  `PROJECT_ID.LOCATION.BUCKET_ID.LOG_VIEW_1` a
JOIN  `PROJECT_ID.LOCATION.BUCKET_ID.LOG_VIEW_2` b
ON
  a.span_id = b.span_id AND
  a.trace = b.trace
LIMIT 100

Query two log views with a union statement

To combine the results of two or more SELECT statements and discard duplicate rows, use the UNION operator. To retain duplicate rows, use the UNION ALL operator:

SELECT
  timestamp, log_name, severity, json_payload, resource, labels
-- Create a union of two log views
FROM(
  SELECT * FROM `PROJECT_ID.LOCATION.BUCKET_ID.LOG_VIEW_1`
  UNION ALL
  SELECT * FROM `PROJECT_ID.LOCATION.BUCKET_ID.LOG_VIEW_2`
)
-- Sort the union by timestamp.
ORDER BY timestamp ASC
LIMIT 100

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.

Remove duplicate log entries

Log Analytics doesn't remove duplicate log entries before a query is run. This behavior is different than when you query log entries by using the Logs Explorer, which removes duplicate entries by comparing the log names, timestamps, and insert ID fields.

You can use row-level validation to remove duplicate log entries.

Note: Queries that perform row-level validation are slower than those that don't. Don't deduplicate log entries unless the query is sensitive to duplicate log entries or the query result must not contain duplicate entries.

For more information, see Troubleshoot: There are duplicate log entries in my Log Analytics results.

What's next

• Query and analyze telemetry with Log Analytics
• Chart SQL query results.
• Save and share queries.

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

• Functions, operators, and conditionals.
• Search functions.
• Query syntax.