@aws-cdk/aws-logs

What is @aws-cdk/aws-logs?

@aws-cdk/aws-logs is an AWS Cloud Development Kit (CDK) library that allows you to define and manage AWS CloudWatch Logs resources using code. It provides a high-level, object-oriented abstraction to create and manage log groups, log streams, and metric filters, among other CloudWatch Logs features.

What are @aws-cdk/aws-logs's main functionalities?

Create a Log Group

This code sample demonstrates how to create a new CloudWatch Log Group with a specified retention period using the @aws-cdk/aws-logs package.

const logs = require('@aws-cdk/aws-logs');
const cdk = require('@aws-cdk/core');

const app = new cdk.App();
const stack = new cdk.Stack(app, 'MyStack');

new logs.LogGroup(stack, 'MyLogGroup', {
  logGroupName: '/aws/my-log-group',
  retention: logs.RetentionDays.ONE_WEEK
});

app.synth();

Create a Log Stream

This code sample demonstrates how to create a new CloudWatch Log Stream within an existing Log Group using the @aws-cdk/aws-logs package.

const logs = require('@aws-cdk/aws-logs');
const cdk = require('@aws-cdk/core');

const app = new cdk.App();
const stack = new cdk.Stack(app, 'MyStack');

const logGroup = new logs.LogGroup(stack, 'MyLogGroup', {
  logGroupName: '/aws/my-log-group',
  retention: logs.RetentionDays.ONE_WEEK
});

new logs.LogStream(stack, 'MyLogStream', {
  logGroup: logGroup,
  logStreamName: 'my-log-stream'
});

app.synth();

Create a Metric Filter

This code sample demonstrates how to create a Metric Filter for a CloudWatch Log Group that filters log events containing the term 'ERROR' and increments a custom metric in CloudWatch.

const logs = require('@aws-cdk/aws-logs');
const cdk = require('@aws-cdk/core');

const app = new cdk.App();
const stack = new cdk.Stack(app, 'MyStack');

const logGroup = new logs.LogGroup(stack, 'MyLogGroup', {
  logGroupName: '/aws/my-log-group',
  retention: logs.RetentionDays.ONE_WEEK
});

new logs.MetricFilter(stack, 'MyMetricFilter', {
  logGroup: logGroup,
  metricNamespace: 'MyNamespace',
  metricName: 'MyMetric',
  filterPattern: logs.FilterPattern.allTerms('ERROR'),
  metricValue: '1'
});

app.synth();

Other packages similar to @aws-cdk/aws-logs

winston-cloudwatch

winston-cloudwatch is a transport for the winston logging library that allows you to send log messages to AWS CloudWatch Logs. Unlike @aws-cdk/aws-logs, which is used for defining and managing CloudWatch Logs resources, winston-cloudwatch is used for sending log data to CloudWatch Logs from your application.

bunyan-cloudwatch

bunyan-cloudwatch is a stream for the Bunyan logging library that sends log records to AWS CloudWatch Logs. Similar to winston-cloudwatch, it focuses on sending log data to CloudWatch Logs rather than managing the log resources themselves.

log4js-cloudwatch-appender

log4js-cloudwatch-appender is an appender for the log4js logging library that sends log events to AWS CloudWatch Logs. It is used for integrating log4js with CloudWatch Logs to send log data, in contrast to @aws-cdk/aws-logs, which is used for infrastructure management.

README

Amazon CloudWatch Logs Construct Library

---

---

This library supplies constructs for working with CloudWatch Logs.

Log Groups/Streams

The basic unit of CloudWatch is a Log Group. Every log group typically has the same kind of data logged to it, in the same format. If there are multiple applications or services logging into the Log Group, each of them creates a new Log Stream.

Every log operation creates a "log event", which can consist of a simple string or a single-line JSON object. JSON objects have the advantage that they afford more filtering abilities (see below).

The only configurable attribute for log streams is the retention period, which configures after how much time the events in the log stream expire and are deleted.

The default retention period if not supplied is 2 years, but it can be set to one of the values in the RetentionDays enum to configure a different retention period (including infinite retention).

retention example

Subscriptions and Destinations

Log events matching a particular filter can be sent to either a Lambda function or a Kinesis stream.

If the Kinesis stream lives in a different account, a CrossAccountDestination object needs to be added in the destination account which will act as a proxy for the remote Kinesis stream. This object is automatically created for you if you use the CDK Kinesis library.

Create a SubscriptionFilter, initialize it with an appropriate Pattern (see below) and supply the intended destination:

const fn = new lambda.Function(this, 'Lambda', { ... });
const logGroup = new LogGroup(this, 'LogGroup', { ... });

new SubscriptionFilter(this, 'Subscription', {
    logGroup,
    destination: new LogsDestinations.LambdaDestination(fn),
    filterPattern: FilterPattern.allTerms("ERROR", "MainThread")
});

Metric Filters

CloudWatch Logs can extract and emit metrics based on a textual log stream. Depending on your needs, this may be a more convenient way of generating metrics for you application than making calls to CloudWatch Metrics yourself.

A MetricFilter either emits a fixed number every time it sees a log event matching a particular pattern (see below), or extracts a number from the log event and uses that as the metric value.

Example:

metricfilter example

Remember that if you want to use a value from the log event as the metric value, you must mention it in your pattern somewhere.

A very simple MetricFilter can be created by using the logGroup.extractMetric() helper function:

logGroup.extractMetric('$.jsonField', 'Namespace', 'MetricName');

Will extract the value of jsonField wherever it occurs in JSON-structed log records in the LogGroup, and emit them to CloudWatch Metrics under the name Namespace/MetricName.

Exposing Metric on a Metric Filter

You can expose a metric on a metric filter by calling the MetricFilter.metric() API. This has a default of statistic = 'avg' if the statistic is not set in the props.

const mf = new MetricFilter(this, 'MetricFilter', {
  logGroup,
  metricNamespace: 'MyApp',
  metricName: 'Latency',
  filterPattern: FilterPattern.exists('$.latency'),
  metricValue: '$.latency',
});

//expose a metric from the metric filter
const metric = mf.metric();

//you can use the metric to create a new alarm
new Alarm(this, 'alarm from metric filter', {
  metric,
  threshold: 100,
  evaluationPeriods: 2,
});

Patterns

Patterns describe which log events match a subscription or metric filter. There are three types of patterns:

• Text patterns
• JSON patterns
• Space-delimited table patterns

All patterns are constructed by using static functions on the FilterPattern class.

In addition to the patterns above, the following special patterns exist:

• FilterPattern.allEvents(): matches all log events.
• FilterPattern.literal(string): if you already know what pattern expression to use, this function takes a string and will use that as the log pattern. For more information, see the Filter and Pattern Syntax.

Text Patterns

Text patterns match if the literal strings appear in the text form of the log line.

• FilterPattern.allTerms(term, term, ...): matches if all of the given terms (substrings) appear in the log event.
• FilterPattern.anyTerm(term, term, ...): matches if all of the given terms (substrings) appear in the log event.
• FilterPattern.anyGroup([term, term, ...], [term, term, ...], ...): matches if all of the terms in any of the groups (specified as arrays) matches. This is an OR match.

Examples:

// Search for lines that contain both "ERROR" and "MainThread"
const pattern1 = FilterPattern.allTerms('ERROR', 'MainThread');

// Search for lines that either contain both "ERROR" and "MainThread", or
// both "WARN" and "Deadlock".
const pattern2 = FilterPattern.anyGroup(
    ['ERROR', 'MainThread'],
    ['WARN', 'Deadlock'],
    );

JSON Patterns

JSON patterns apply if the log event is the JSON representation of an object (without any other characters, so it cannot include a prefix such as timestamp or log level). JSON patterns can make comparisons on the values inside the fields.

• Strings: the comparison operators allowed for strings are = and !=. String values can start or end with a * wildcard.
• Numbers: the comparison operators allowed for numbers are =, !=, <, <=, >, >=.

Fields in the JSON structure are identified by identifier the complete object as $ and then descending into it, such as $.field or $.list[0].field.

• FilterPattern.stringValue(field, comparison, string): matches if the given field compares as indicated with the given string value.
• FilterPattern.numberValue(field, comparison, number): matches if the given field compares as indicated with the given numerical value.
• FilterPattern.isNull(field): matches if the given field exists and has the value null.
• FilterPattern.notExists(field): matches if the given field is not in the JSON structure.
• FilterPattern.exists(field): matches if the given field is in the JSON structure.
• FilterPattern.booleanValue(field, boolean): matches if the given field is exactly the given boolean value.
• FilterPattern.all(jsonPattern, jsonPattern, ...): matches if all of the given JSON patterns match. This makes an AND combination of the given patterns.
• FilterPattern.any(jsonPattern, jsonPattern, ...): matches if any of the given JSON patterns match. This makes an OR combination of the given patterns.

Example:

// Search for all events where the component field is equal to
// "HttpServer" and either error is true or the latency is higher
// than 1000.
const pattern = FilterPattern.all(
    FilterPattern.stringValue('$.component', '=', 'HttpServer'),
    FilterPattern.any(
        FilterPattern.booleanValue('$.error', true),
        FilterPattern.numberValue('$.latency', '>', 1000)
    ));

Space-delimited table patterns

If the log events are rows of a space-delimited table, this pattern can be used to identify the columns in that structure and add conditions on any of them. The canonical example where you would apply this type of pattern is Apache server logs.

Text that is surrounded by "..." quotes or [...] square brackets will be treated as one column.

• FilterPattern.spaceDelimited(column, column, ...): construct a SpaceDelimitedTextPattern object with the indicated columns. The columns map one-by-one the columns found in the log event. The string "..." may be used to specify an arbitrary number of unnamed columns anywhere in the name list (but may only be specified once).

After constructing a SpaceDelimitedTextPattern, you can use the following two members to add restrictions:

• pattern.whereString(field, comparison, string): add a string condition. The rules are the same as for JSON patterns.
• pattern.whereNumber(field, comparison, number): add a numerical condition. The rules are the same as for JSON patterns.

Multiple restrictions can be added on the same column; they must all apply.

Example:

// Search for all events where the component is "HttpServer" and the
// result code is not equal to 200.
const pattern = FilterPattern.spaceDelimited('time', 'component', '...', 'result_code', 'latency')
    .whereString('component', '=', 'HttpServer')
    .whereNumber('result_code', '!=', 200);

Notes

Be aware that Log Group ARNs will always have the string :* appended to them, to match the behavior of the CloudFormation AWS::Logs::LogGroup resource.

Changelog

1.61.1 (2020-08-28)

Bug Fixes

• cli: unable to upgrade new style bootstrap to version (#10030) (8d3e422), closes #10016