Sample SQL queries

This document contains sample queries over log entries that are stored in log buckets that are upgraded to use Log Analytics. On these buckets you can run SQL queries from the Log Analytics page in the Google Cloud console. For more samples, see the logging-analytics-samples and the security-analytics GitHub repositories.

This document doesn't describe SQL or how to route and store log entries. For information about those topics, see the What's next section.

The examples on this page query log views. 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.

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 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)

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

Before you begin

This section describes steps that you must complete before you can use Log Analytics.

Configure log buckets

Ensure that your log buckets have been upgraded to use Log Analytics:

1. In the Google Cloud console, go to the Logs Storage page:

   Go to Logs Storage

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

2. For each log bucket that has a log view that you want to query, ensure that the Log Analytics available column displays Open. If Upgrade is shown, then click Upgrade and complete the dialog.

Configure IAM roles and permissions

This section describes the IAM roles or permissions that are required to use Log Analytics:

• To get the permissions that you need to use Log Analytics and query log views, ask your administrator to grant you the following IAM roles on your project:

  • To query the _Required and _Default log buckets: Logs Viewer (roles/logging.viewer)
  • To query all log views in a project: Logs View Accessor (roles/logging.viewAccessor)

  You can restrict a principal to a specific log view either by adding an IAM condition to the Logs View Accessor role grant made at the project level, or by adding an IAM binding to the policy file of the log view. For more information, see Control access to a log view.

  These are the same permissions that you need to view log entries on the Logs Explorer page. For information about additional roles that you need to query views on user-defined buckets or to query the _AllLogs view of the _Default log bucket, see Cloud Logging roles.

• To get the permissions that you need to query analytics views, ask your administrator to grant you the Observability Analytics User (roles/observability.analyticsUser) IAM role on your project.

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 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.

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

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.

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
  -- Don't compare proto_payload to NULL. The proto_payload has a data type of RECORD.
  WHERE json_payload IS NULL AND text_payload IS 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 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.

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.

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.

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

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

For information about how to route and store log entries, see the following documents:

• Create a log bucket
• Upgrade a bucket to use Log Analytics
• Link a log bucket to a BigQuery dataset
• Configure and manage sinks

For SQL reference documentation, see the following documents:

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