Optic

A powerful, highly extensible and easy to use logging framework for Deno.

At a glance

Easy to use fluid interface
Log anything
Modular and highly extensible - build your own streams, filters, transformers, monitors, formatters and more
Deferred log message resolution for greater performance
Filters - keep your logs clean
Transformers - hide sensitive data, strip new lines, encode data, etc.
Monitors - watch your logs and take action
Profiling - measure the performance of your code

Quick start
Logging
Streams
Log formatting
Monitors
Transformers
Filters
Profiling

Quick start

Simple example

import { Logger } from "https://deno.land/x/optic/mod.ts";

const logger = new Logger();
logger.info("Hello world!"); // outputs log record to the console

Complete example

import {
  every,
  FileStream,
  of,
} from "https://deno.land/x/optic/streams/fileStream/mod.ts";
import {
  between,
  Level,
  Logger,
  LogRecord,
  Stream,
  TimeUnit,
} from "https://deno.land/x/optic/mod.ts";
import { JsonFormatter } from "https://deno.land/x/optic/formatters/mod.ts";
import { PropertyRedaction } from "https://deno.land/x/optic/transformers/propertyRedaction.ts";

// Configure the output file stream
const fileStream = new FileStream("logFile.txt")
  .withMinLogLevel(Level.Warn)
  .withFormat(
    new JsonFormatter()
      .withPrettyPrintIndentation(2)
      .withDateTimeFormat("YYYY.MM.DD hh:mm:ss:SSS"),
  )
  .withBufferSize(10000)
  .withLogFileInitMode("append")
  .withLogFileRotation(
    every(200000).bytes().withLogFileRetentionPolicy(of(7).days()),
  )
  .withLogHeader(true)
  .withLogFooter(true);

// Configure the logger
const log = new Logger()
  .withMinLogLevel(Level.Warn)
  .addFilter((stream: Stream, logRecord: LogRecord) => logRecord.msg === "spam")
  .addTransformer(new PropertyRedaction("password"))
  .addStream(fileStream);

// "info" is lower than configured min log level of "warn"
log.info("Level too low. This won't be logged");

// logs "Hello World" and supporting metadata, returns "Hello world"
const logVal: string = log.critical("Hello world", 12, true, { name: "Poe" });

// Log records with `msg` of "spam" are filtered out
log.warn("spam");

// logs `msg` as { "user": "jsmith", "password": "[Redacted]" }
log.warn({ user: "jsmith", password: "secret_password" });

// debug < min log level, so function isn't evaluated and error not thrown
log.debug(() => {
  throw new Error("I'm not thrown");
});

// error > min log level, so function is evaluated and `msg` is set to "1234"
log.error(() => {
  return "1234";
}); // logs "1234"

const x = 5;
log.if(x > 10).error("Since x < 10 this doesn't get logged");

log.mark("before loop");
for (let i = 0; i < 1000000; i++) {
  log.every(100).warn("Logs every 100th iteration");
  log.atMostEvery(10, TimeUnit.SECONDS).warn(
    "Logs at most once every 10 seconds",
  );
}
log.mark("after loop");
log.profilingConfig().withLogLevel(Level.Warn);
// logs (at warn level) time, memory usage and ops called for loop duration
log.measure(between("before loop", "after loop"));

log.enabled(false);
log.error("Logger is disabled, so this does nothing");

Logging

All logging in Optic is done through a logger instance, which provides the interface and framework for all logging activity.

In this section:

Creating a logger
Sharing loggers across modules
Logging an event
Log levels
Log records
Minimum log level
Logging lifecycle
In-line logging
Deferred logging
Conditional logging
Deduplicating logs
Rate limiting logs
Disabling the logger

Creating a logger

Before you can log anything you must first get an instance of a logger.

// Using an unnamed logger
const defaultLogger = new Logger();

// Using a named logger
const configLogger = new Logger("config");

To reuse the same logger instance across multiple modules, declare and configure your loggers in their own module which are then exported for use in other modules. E.g.

/** logger.ts */
import { ConsoleStream, Logger } from "https://deno.land/x/optic/mod.ts";
import { JsonFormatter } from "https://deno.land/x/optic/formatters/json.ts";

export const logger = new Logger();
logger.addStream(new ConsoleStream().withFormat(new JsonFormatter()));

/** module_a.ts */
import { logger } from "./logger.ts";

logger.info("hello world");

Logging an event

You can log an event through any of the level functions on the logger, supplying a msg (of any type) and one or more optional metadata items. E.g.

logger.info("File loaded", "exa_113.txt", 1223, true);

In this example, "File loaded" is the log record message (primary data), with supporting metadata of the file name ("exa_113.txt"), size (1223) and readonly attribute (true) supplied.

Log levels

Optic supports the following logging levels out of the box:

Trace
Debug
Info
Warn
Error
Critical

These may be used directly via the logger, e.g.

logger.trace("Some trace info");
logger.error("Oops, something went wrong");

Or through the use of the Level enum, e.g.

logger.log(Level.Info, "Here some info");

Log Records

Each log event (e.g. logger.info("hello world")) generates a LogRecord with all the relevant info on the event. Fields captured in the LogRecord are:

Field	Description
msg	The primary content of the log event of any type
metadata	Supporting data, in one or more additional data items of any type
dateTime	When the log record was created
level	The log level of the event
logger	The name of the logger which generated the event

Minimum log level

Each logger can be configured to log at a minimum level (the default level is Debug). Log events with a level lower than the minimum level are ignored with no action taken. There are 3 ways in which you can configure a logger to log at a minimum level:

Programmatically

Within the code, this can be set at any time and takes highest precedence of any method:

logger.withLevel(Level.Warn);

Environment variable

Through the use of an environment variable OPTIC_MIN_LEVEL you can set the minimum log level of any logger. This method takes lowest priority and will be overridden if set programmatically or supplied via a command line argument. The values for this variable are any of the logging levels in uppercase, e.g. Info.

NOTE for this method to work you MUST supply --allow-env to the Deno command line process. E.g.:

OPTIC_MIN_LEVEL=Error deno run --allow-env my-module.ts

Command line argument

You may also set the value of the minimum log level via a command line argument, minLogLevel. Minimum log levels set this way apply to all loggers unless overridden by programmatically setting a new level. Example:

deno run my-module.ts minLogLevel=Error

The value of the argument is any valid log level in Pascal case.

Logging lifecycle

Logging events will undergo the following lifecycle:

If the minimum log level requirement is not met, the msg is returned with no actions undertaken
Resolve the msg function value if using deferred logging (see below)
Run each registered monitor against the log record
For each stream
- Run each registered filter
- Run each registered transformer against the log record (if not filtered)
- Pass log record to stream for handling (if not filtered)
Return msg value (or resolved msg value in deferred logging)

In-line logging

All log statements return the value of the msg field, allowing more concise coding. E.g.

const user: User = logger.info(getUser());

// is equivalent to:
const user: User = getUser();
logger.info(user);

Deferred logging

Deferred logging is used when you have expensive objects to create or calculate for logging purposes, but don’t want to incur the cost if the log message won’t be handled anyway. By supplying a function argument to the log event msg field, this will defer resolution of the value of this function until after determining if the log event should be recorded. The resolved value is then set as the msg field in the LogRecord.

Example:

const value = logger.info(() => {
  return expensiveObjectCreation();
});

Here, expensiveObjectCreation() won’t be called unless the logger is allowed to log info messages. value, in this example, will be set to the return value of expensiveObjectCreation() if the logger logged the message or undefined if it did not log it.

Conditional logging

You can specify a condition which must be met to log the log message. To do this, supply a boolean condition to the if function on the logger. E.g.

logger.if(attempts > 3).warn("Excessive attempts by user");

Note that even if the condition is true, the log record may not be logged if the minimum log level for the logger (and/or stream) is higher than this record.

Deduplicating log messages

You can use the logger to deduplicate log messages. When enabled, if 3 or more log messages in a row are the same (same msg and supporting meta data) then output of the duplicates is suppressed. Once a new log message which is different is logged, then a new log message is finally cut (ahead of the new/different one) detailing the number of duplicated log messages which were suppressed. This can clean up your logs in situations where significant duplicates are encountered. Additionally, significant performance gains can potentially be had by not recording duplicate log messages over and over. Be aware, however, that comparing the current log message to the previous isn’t free and if your logs have no or limited duplicates the performance may be reduced. If performance is critical it is suggested that you test the deduplication capabilities. This feature is disabled by default.

To turn on deduping of logs:

logger.withDedupe();

To turn off deduping of logs:

logger.withDedupe(false);

Example:

const logger = new Logger().withDedupe();
for (let i = 0; i < 1000; i++) {
  logger.info("hello world");
}

Output:

2021-01-11T22:32:58.497Z Info  hello world 
2021-01-11T22:32:58.506Z Info    ^-- last log repeated 999 additional times

Rate limiting the logger

You can limit how many times the logger logs a particular statement in one of two ways;

logger.atMostEvery(5, TimeUnit.SECONDS).info(
  "I'm only logged at most every 5 seconds",
);
logger.every(100).info("I'm logged every 100 attempts");

An important note: Rate limiters work in a context. The context for the rate limiting is determined, by default, on the amount, unit (for atMostEvery) and log level. Where two or more every or atMostEvery statements match the same context, the same rate limiter will be used, possibly causing unintended side effects through race conditions on which of the statements will be logged when matching or exceeding the constraint. To avoid this, you can enforce unique contexts by passing in an optional context string:

logger.atMostEvery(5, TimeUnit.SECONDS, "Context 1").info(
  "Logged at most every 5 seconds",
);
logger.atMostEvery(5, TimeUnit.SECONDS, "Context 2").info(
  "Also logged at most every 5 seconds",
);

Disabling the logger

You can programmatically disable the logger which transforms it into a no-op logger. This is useful for loggers which should not run in a production environment for example and the value of enabled() can be supplied via an environment variable or argument. The logger can later be re-enabled. When disabled:

Logs are not logged and deferred log messages are not resolved
Adding or removing streams, filters, monitors or transformers is silently ignored and they are not added (or removed)
Changes to the minimum log level are silently ignored

The only action the logger will undertake when disabled is, upon module unload, to carry out any tear down (e.g. destroy()) on any streams, filters, monitors or transformers which were registered before the logger was disabled.

To disable the logger:

logger.enabled(false);

Streams

Streams in Optic control the flow of log records from a module logging statement to an endpoint defined by the stream (e.g. console, file system, etc.). A logger can have one or more streams and the same log message will be handled by all registered streams (unless filtered from that stream).

ConsoleStream is the default stream in a logger. Once any stream is added to the logger, this default stream is removed. If console logging is still desired as an additional stream, you should explicitly add the ConsoleStream.

Optics streams

There are two out of the box streams available.

Console stream

A basic stream which outputs log messages to the console.

import { ConsoleStream, Level, Logger } from "https://deno.land/x/optic/mod.ts";
import { TokenReplacer } from "https://deno.land/x/optic/formatters/mod.ts";

const consoleStream = new ConsoleStream()
  .withMinLogLevel(Level.Debug)
  .withLogHeader(true)
  .withLogFooter(true)
  .withFormat(
    new TokenReplacer()
      .withColor()
      .withDateTimeFormat("YYYY.MM.DD hh:mm:ss:SSS"),
  );

const logger = new Logger().addStream(consoleStream);

See Formatting for further detail on formatting your logs.

File stream

A stream which outputs log messages to the file system.

import { Level, Logger } from "https://deno.land/x/optic/mod.ts";
import { JsonFormatter } from "https://deno.land/x/optic/formatters/mod.ts";
import {
  every,
  FileStream,
  of,
} from "https://deno.land/x/optic/streams/fileStream/mod.ts";

const fileStream = new FileStream("./logFile.txt")
  .withMinLogLevel(Level.Warn)
  .withFormat(new JsonFormatter())
  .withBufferSize(30000)
  .withLogFileInitMode("append")
  .withLogFileRotation(
    every(2000000).bytes().withLogFileRetentionPolicy(of(7).days()),
  )
  .withLogHeader(true)
  .withLogFooter(true);

const logger = new Logger().addStream(fileStream);

See FileStream documentation for full details. See also Formatting for further detail on formatting your logs.

Defining a custom stream

You can build your own stream by creating a class which implements the Stream interface. The handle function is the only requirement which defines what your stream should do with a log record (and return true if the record was handled).

Basic example:

import { Logger, LogRecord, Stream } from "https://deno.land/x/optic/mod.ts";

class SimpleStream implements Stream {
  handle(logRecord: LogRecord): boolean {
    console.log(logRecord.msg);
    return true;
  }
}

const logger = new Logger().addStream(new SimpleStream());

Streams can also take logging metadata in logHeader() and logFooter() functions, and also can expose setup() and destroy() functions.

Log formatting

Optic’s streams allow you to format your logs however you wish, either through your own custom formatting or several out of the box formatters. Formatters are set directly on the stream via withFormat().

Optic formatters overview

Three out of the box formatters are available. See also the complete documentation on formatters.

TokenReplacer formatter

This formatter allows you to construct a custom string using tokens as placeholders for the various log record fields.

Example:

import { ConsoleStream, Logger } from "https://deno.land/x/optic/mod.ts";
import { TokenReplacer } from "https://deno.land/x/optic/formatters/mod.ts";

const logger = new Logger().addStream(
  new ConsoleStream()
    .withFormat(
      new TokenReplacer()
        .withFormat("{dateTime} {level} {msg} {metadata}")
        .withDateTimeFormat("hh:mm:ss YYYY-MM-DD")
        .withLevelPadding(10)
        .withColor(),
    ),
);

See TokenReplacer documentation for full details.

JSON formatter

This formatter allows you to output your log record as a structured JSON formatted string.

Example:

import { ConsoleStream, Logger } from "https://deno.land/x/optic/mod.ts";
import { JsonFormatter } from "https://deno.land/x/optic/formatters/mod.ts";

const logger = new Logger().addStream(
  new ConsoleStream()
    .withFormat(
      new JsonFormatter()
        .withFields(["dateTime", "level", "logger", "msg"])
        .withDateTimeFormat("hh:mm:ss YYYY-MM-DD")
        .withPrettyPrintIndentation(2),
    ),
);

See JSON formatter documentation for full details.

DateTimeFormatter

A formatter to be used within other formatters, this allows you to provide a custom format for your date/time fields. Example:

logger.addStream(
  new ConsoleStream()
    .withFormat(
      new JsonFormatter().withDateTimeFormat("hh:mm:ss YYYY-MM-DD"),
    ),
);

See DateTimeFormatter for full details.

Custom formatters

You can also easily supply your own custom formatter by implementing the Formatter interface. See Using your own custom formatter for full details.

Monitors

Monitors allow you to spy on log records that flow through your logger. Monitors are run first, before any filtering, transformation or stream handling.

Some use cases for monitors include:

Collect stats of your log records
Send alert if too many error records detected
Take automated action on specific error scenario
Debugging aid - e.g. output certain records to the console

Constructing a monitor

There are two ways to construct a monitor:

Monitor function

This is a good choice for short and simple monitors. Monitor functions must match the following type:

export type MonitorFn = (logRecord: LogRecord) => void;

Example:

import { LogRecord, MonitorFn } from "https://deno.land/x/optic/mod.ts";

const mon: MonitorFn = (logRecord: LogRecord): void => {
  if ((logRecord.msg as User).username === "jsmith") {
    console.log("User jsmith spotted again");
  }
};

Implement the Monitor interface

The Monitor interface requires implementation of the check function which is of type MonitorFn as above. This gives you the power of a class for more complex monitors.

import { LogRecord, Monitor } from "https://deno.land/x/optic/mod.ts";

class UserMonitor implements Monitor {
  check(logRecord: LogRecord): void {
    if ((logRecord.msg as User).username === "jsmith") {
      console.log("User jsmith spotted again");
    }
  }
}

Registering Monitors

Monitors are registered directly with the logger as follows:

const logger = new Logger().addMonitor(new UserMonitor());

Transformers

Optic allows you to transform log records sent to a stream, allowing a log record to be transformed in one stream but not another. Transformation can change some, all or none of the original log record. Transformation takes place after monitors and also after log filtering but before the log record is sent to a stream.

Some use cases for transformation include:

Hiding sensitive data in your logs such as passwords or credit card details
Obscuring personal information, complying with data protection laws
Strip new lines from log data (log forging protection)
Encoding data
Compressing data

Constructing a transformer

There are two ways to construct an transformer.

Transformer function

This is a good choice for short and simple transformers. Transformer functions must match the following type:

export type TransformerFn = (stream: Stream, logRecord: LogRecord) => LogRecord;

The function takes a stream and logRecord and returns either the original log record if nothing is transformed, or a copy of the original with the necessary transformations applied. Example:

import {
  LogRecord,
  Stream,
  TransformerFn,
} from "https://deno.land/x/optic/mod.ts";

const tr: TransformerFn = (
  stream: Stream,
  logRecord: LogRecord,
): LogRecord => ({
  msg: (logRecord.msg as string).startsWith("password:")
    ? "password: [Redacted]"
    : logRecord.msg,
  metadata: [...logRecord.metadata],
  level: logRecord.level,
  logger: logRecord.logger,
  dateTime: new Date(logRecord.dateTime.getTime()),
});

Implement the Transformer interface

The Transformer interface requires implementation of the transform function, which is of type TransformerFn as above. This gives you the power of a class for more complex transformations.

import {
  LogRecord,
  Stream,
  Transformer,
} from "https://deno.land/x/optic/mod.ts";

class PasswordObfuscator implements Transformer {
  transform(stream: Stream, logRecord: LogRecord): LogRecord {
    if ((logRecord.msg as string).startsWith("password:")) {
      return {
        msg: "password: [Redacted]",
        metadata: [...logRecord.metadata],
        level: logRecord.level,
        logger: logRecord.logger,
        dateTime: new Date(logRecord.dateTime.getTime()),
      };
    } else {
      return logRecord;
    }
  }
}

Registering transformers

Transformers are registered directly with the logger as follows:

const passwordObfuscator = new PasswordObfuscator();
const logger = new Logger().addTransformer(passwordObfuscator);

Optic transformers

Two out of the box transformers are available in Optic.

Property redaction obfuscator

This transformer allows you to specify a single object property name which if found in the msg or metadata log record fields (using deep object searching), will replace the value of that property with the string [Redacted]. The original object is untouched, as transformation clones the object before obfuscation.

import { PropertyRedaction } from "https://deno.land/x/optic/mod.ts";

logger.addTransformer(new PropertyRedaction("password"));

// This next record is untouched by the transformer (no `password` property)
logger.info({ user: "abc29002", dateOfBirth: "1966/02/33" });

// This record gets transformed to: {user: "abc29002", password: "[Redacted]"}
logger.info({ user: "abc29002", password: "s3cr3tpwd" });

Regular expression redaction

This obfuscator allows you to specify a regular expression and an optional replacer function. The RegExpReplacer will then go through the msg and metadata fields looking for string values. Anytime it finds one, it will run the Javascript string.replace(regExp, replacer) against it. For more details on this, see String.replace().

There are two included replacer functions. alphaNumericReplacer (the default) will replace all letters and numbers with *’s. nonWhitespaceReplacer will replace all non white space characters with *’s. For both replacers, if the regular expression does not use groups then then entire match is replaced, however if groups are used, only the groups are replaced.

import {
  nonWhitespaceReplacer,
  RegExpReplacer,
} from "https://deno.land/x/optic/transformers/regExpReplacer.ts";
import { Logger } from "https://deno.land/x/optic/mod.ts";

const logger = new Logger()
  .addTransformer(new RegExpReplacer(/£([\d]+\.[\d]{2})/))
  .addTransformer(new RegExpReplacer(/password: (.*)/, nonWhitespaceReplacer));

logger.info("Amount: £122.51"); // becomes "Amount: £***.**" ('£' is not in a group)
logger.info("password: MyS3cret! Pwd!"); // becomes "password: ********* ****"

RegExp and Replacer examples:

RegExp	Test string	alphaNumericReplacer	nonWhitespaceReplacer
/£([\d]+.[\d]{2})/	£52.22	£.	£*****
/\d{2}-\d{2}-\d{4}/	30-04-1954	--****	**********

Filters

Filters allows you to filter out log records from your streams. A log record can be filtered out from one stream but not another. Filters are processed after monitors, but before obfuscators or stream handling. Upon handling a log record, the logger will run filters once for each registered stream.

Some use cases for filters include:

Preventing spam from filling up your logs
Directing log messages to certain streams only, based on content
Blocking malicious log records
Redirecting certain log records to an entirely different logger

Constructing a filter

There are two ways to construct a filter.

Filter function

This is a good choice for short and simple filters. Filter functions must match the following type:

export type FilterFn = (stream: Stream, logRecord: LogRecord) => boolean;

The function takes in a stream and logRecord and returns true if the logRecord should be filtered out. Example:

import { FilterFn, LogRecord, Stream } from "https://deno.land/x/optic/mod.ts";
const filter: FilterFn = (stream: Stream, logRecord: LogRecord) =>
  (logRecord.msg as string).includes("bad stuff");

Implement the Filter interface

The Filter interface requires you to implement the shouldFilterOut function, which is of type FilterFn as above. This gives you the power of a class for more complex filtering, or perhaps you want to redirect filtered out logs to another logger and stream.

import { Filter, LogRecord, Stream } from "https://deno.land/x/optic/mod.ts";

class MyFilter implements Filter {
  shouldFilterOut(stream: Stream, logRecord: LogRecord): boolean {
    return (logRecord.msg as string).includes("bad stuff");
  }
}

Registering filters

Filters are registered directly with the logger as follows:

const myFilter = new MyFilter();
const logger = new Logger().addFilter(myFilter);

Optic filters

Two out of the box filters are available in Optic.

Regular Expression Filter

This filter takes in a regular expression. If it matches, then the log record is filtered out. The log record msg and metadata fields are first converted to a string if necessary before testing the regular expression.

import { Logger } from "https://deno.land/x/optic/mod.ts";
import { RegExpFilter } from "https://deno.land/x/optic/filters/regExpFilter.ts";

// Filters out log records containing `%` or `&` in the message or metadata
const regExpFilter = new RegExpFilter(/[%&]+/);
const logger = new Logger().addFilter(regExpFilter);
logger.error("Oh no!"); // not filtered
logger.error("Oh no!", "& another thing"); // filtered out

Substring filter

This filter takes in a string. If this string is found to be a substring of either the log record msg or metadata fields (converting them to string first if required), then this log record is filtered out. Example:

import { Logger } from "https://deno.land/x/optic/mod.ts";
import { SubStringFilter } from "https://deno.land/x/optic/filters/subStringFilter.ts";

const subStringFilter = new SubStringFilter("user1234");
const logger = new Logger().addFilter(subStringFilter);
logger.info({ user: "joe1944", action: "login" }); // not filtered
logger.info({ user: "user1234", action: "login" }); // filtered out

Profiling

Optic’s profiling capabilities allow you to measure your code’s performance. Profiling is based on marks which are recorded at various points in your code base. A mark is a snapshot of your running application, recording the time, memory consumption and ops calls (these are the calls between the V8 engine and Deno runtime). Measures are then used to output differences between two marks to your logs.

Recording marks

Recording marks in your code is very straightforward. The example below takes a snapshot of the time, memory usage and ops calls and saves these against an identifier (e.g. below).

logger.mark("before");
someFunction();
logger.mark("after");

Measuring

Measuring outputs the difference between two marks to your logs. For example, this will measure the performance of someFunction():

import { between } from "https://deno.land/x/optic/mod.ts";

logger.mark("before");
someFunction();
logger.mark("after");

logger.measure(between("before", "after"), "with description");

Sample output

By default, profiling uses the SummaryMeasureFormatter to format the measure log output. Example output:

//Measuring 'before' -> 'after' (with description), took 790ms; heap usage increased 9.2 MB to 11.7 MB; 18 ops dispatched, all completed

Special marks

There are two special marks which can be used, NOW and PROCESS_START. The special mark NOW will generate a mark at the time of measurement, while PROCESS_START represents the moment the process started (with 0ms time, 0 memory usage and 0 ops calls). These special marks do not require you to use the mark function in the logger first.

Measuring examples

import {
  between,
  from,
  NOW,
  PROCESS_START,
  to,
} from "https://deno.land/x/optic/mod.ts";

logger.mark("before");
someFunction();
logger.mark("after");

//Measure between two marks (with optional description)
logger.measure(between("before", "after"));
logger.measure(between(PROCESS_START, "after"));
logger.measure(between("before", NOW), "with description");

//Measure from a mark to NOW (with optional description)
logger.measure(from("before"));
logger.measure(from("before"), "with description");

//Measure from PROCESS_START to mark (with optional description)
logger.measure(to("after"));
logger.measure(to("after"), "with description");

//Mesure from PROCESS_START to NOW (with optional description)
logger.measure();
logger.measure("with description");

Configuring the profiler

There are a number of options for you to configure the profiler with if you wish.

//Defaults shown below
logger.profilingConfig()
  .enabled(true) //Enable or disable all recording of marks or measures
  .captureMemory(true) //Enable or disable capturing of memory information
  .captureOps(true) //Enable or disable capturing of ops calls
  .withLogLevel(Level.Info) //Set the log level at which the profile measure is output
  .withFormatter(new SummaryMeasureFormatter()); //Formats the profiling log message

Custom measure output

As described above, by default the SummaryMeasureFormatter is used to format the output. You can specify your own measure formatter by implementing the interface MeasureFormatter:

interface MeasureFormatter<T> {
  format(startMark: ProfileMark, endMark: ProfileMark, label?: string): T;
}

Example:

import { MeasureFormatter } from "https://deno.land/x/optic/mod.ts";

const myFormatter: MeasureFormatter<string> = {
  format(startMark: ProfileMark, endMark: ProfileMark, label?: string): string {
    return (endMark.timestamp - startMark.timestamp) + "ms" +
      (label ? (" " + label) : "");
  },
};
log.profilingConfig().withFormatter(myFormatter);

Optic

At a glance

Table of contents

Quick start

Simple example

Complete example

Logging

Creating a logger

Sharing loggers across modules

Logging an event

Log levels

Log Records

Minimum log level

Programmatically

Environment variable

Command line argument

Logging lifecycle

In-line logging

Deferred logging

Conditional logging

Deduplicating log messages

Rate limiting the logger

Disabling the logger

Streams

Optics streams

Console stream

File stream

Defining a custom stream

Log formatting

Optic formatters overview

TokenReplacer formatter

JSON formatter

DateTimeFormatter

Custom formatters

Monitors

Constructing a monitor

Monitor function

Implement the Monitor interface

Registering Monitors

Transformers

Constructing a transformer

Transformer function

Implement the Transformer interface

Registering transformers

Optic transformers

Property redaction obfuscator

Regular expression redaction

Filters

Constructing a filter

Filter function

Implement the Filter interface

Registering filters

Optic filters

Regular Expression Filter

Substring filter

Profiling

Recording marks

Measuring

Sample output

Special marks

Measuring examples

Configuring the profiler

Custom measure output