stats

Use stats to create visualizations of your log data such as bar charts, line charts, and stacked area charts. This helps you more efficiently identify patterns in your log data. CloudWatch Logs Insights generates visualizations for queries that use the stats function and one or more aggregation functions.

For example, the following query in a Route 53 log group returns visualizations showing the distribution of Route 53 records per hour, by query type.

stats count(*) by queryType, bin(1h)

All such queries can produce bar charts. If your query uses the bin() function to group the data by one field over time, you can also see line charts and stacked area charts.

Visualize time series data

Time series visualizations work for queries with the following characteristics:

These queries can produce line charts, stacked area charts, bar charts, and pie charts.

Examples

For a complete tutorial, see Tutorial: Run a query that produces a time series visualization.

Here are more example queries that work for time series visualization.

The following query generates a visualization of the average values of the myfield1 field, with a data point created every five minutes. Each data point is the aggregation of the averages of the myfield1 values from the logs from the previous five minutes.

stats avg(myfield1) by bin(5m)

The following query generates a visualization of three values based on different fields, with a data point created every five minutes. The visualization is generated because the query contains aggregate functions and uses bin() as the grouping field.

stats avg(myfield1), min(myfield2), max(myfield3) by bin(5m)

Line chart and stacked area chart restrictions

Queries that aggregate log entry information but don't use the bin() function can generate bar charts. However, the queries cannot generate line charts or stacked area charts. For more information about these types of queries, see Visualize log data grouped by fields.

Visualize log data grouped by fields

You can produce bar charts for queries that use the stats function and one or more aggregation functions. For more information, see Aggregation Functions in the Stats Command.

To see the visualization, run your query. Then choose the Visualization tab, select the arrow next to Line, and choose Bar. Visualizations are limited to up to 100 bars in the bar chart.

Examples

For a complete tutorial, see Tutorial: Run a query that produces a visualization grouped by log fields. The following paragraphs include more example queries for visualization by fields.

The following VPC flow log query finds the average number of bytes transferred per session for each destination address.

stats avg(bytes) by dstAddr

You can also produce a chart that includes more than one bar for each resulting value. For example, the following VPC flow log query finds the average and maximum number of bytes transferred per session for each destination address.

stats avg(bytes), max(bytes) by dstAddr

The following query finds the number of Amazon Route 53 query logs for each query type.

stats count(*) by queryType

Use multiple stats commands in a single query

You can use as many as two stats commands in a single query. This enables you to perform an additional aggregation on the output of the first aggregation.

Example: Query with two stats commands

For example, the following query first find the total traffic volume in 5-minute bins, then calculates the highest, lowest, and average traffic volume among those 5-minute bins.

FIELDS strlen(@message) AS message_length
| STATS sum(message_length)/1024/1024 as logs_mb BY bin(5m)
| STATS max(logs_mb) AS peak_ingest_mb, 
        min(logs_mb) AS min_ingest_mb, 
        avg(logs_mb) AS avg_ingest_mb

Example: Combine multiple stats commands with other functions such as filter, fields, bin

You can combine two stats commands with other commands such as filter and fields in a single query. For example, the following query finds the number of distinct IP addresses in sessions and finds the number of sessions by client platform, filters those IP addresses, and then finally finds the average of session requests per client platform.

STATS count_distinct(client_ip) AS session_ips, 
      count(*) AS requests BY session_id, client_platform
| FILTER session_ips > 1
| STATS count(*) AS multiple_ip_sessions, 
        sum(requests) / count(*) AS avg_session_requests BY client_platform

You can use bin and dateceil functions in queries with multiple stats commands. For example, the following query first combines messages into 5-minute blocks, then aggregates those 5-minute blocks into 10-minute blocks and calculates the highest, lowest, and average traffic volumes within each 10-minute block.

FIELDS strlen(@message) AS message_length
| STATS sum(message_length) / 1024 / 1024 AS logs_mb BY BIN(5m) as @t
| STATS max(logs_mb) AS peak_ingest_mb, 
        min(logs_mb) AS min_ingest_mb,
        avg(logs_mb) AS avg_ingest_mb BY dateceil(@t, 10m)

Notes and limitations

A query can have a maximum of two stats commands. This quota can't be changed.

If you use a sort or limit command, it must appear after the second stats command. If it is before the second stats command, the query is not valid.

When a query has two stats commands, the partial results from the query do not begin displaying until the first stats aggregation is complete.

In the second stats command in a single query, you can refer only to fields that are defined in the first stats command. For example, the following query is not valid because the @message field won't be available after the first stats aggregation.

FIELDS @message
| STATS SUM(Fault) by Operation
# You can only reference `SUM(Fault)` or Operation at this point
| STATS MAX(strlen(@message)) AS MaxMessageSize # Invalid reference to @message

Any fields that you reference after the first stats command must be defined in that first stats command.

STATS sum(x) as sum_x by y, z
| STATS max(sum_x) as max_x by z
# You can only reference `max(sum_x)`, max_x or z at this point

FIELDS strlen(@message) AS message_length
 | STATS sum(message_length) AS ingested_bytes BY @logStream
 | STATS avg(ingested_bytes) BY bin(5m) # Invalid reference to @timestamp field

FIELDS strlen(@message) AS message_length
 | STATS sum(message_length) AS ingested_bytes, max(@timestamp) as @t BY @logStream
 | STATS avg(ingested_bytes) BY dateceil(@t, 5m)

Functions to use with stats

CloudWatch Logs Insights supports both stats aggregation functions and stats non-aggregation functions.

Use statsaggregation functions in the stats command and as arguments for other functions.

Stats non-aggregation functions

Use non-aggregation functions in the stats command and as arguments for other functions.