logql-jsonnet

A Jsonnet based DSL for writing LogQL queries, inspired by promql-jsonnet. This is useful when creating Grafana dashboards using grafonnet. Instead of having to template strings manually, you can now use immutable builders to DRY out your LogQL queries.

API Reference and LogQL Support

Log Stream Selectors

LogQL Stream Selector Documentation

Operation	Description
`.withLabels(object)`	Accepts an object using the key as the label and the value as the value of the predicate, only uses equality `=` Usage: `.withLabels({cluster: "primary",region: 'us-east-1'})`
`.withLabel(label, op, value)`	Sets a single label selector, with specific arguments for the `label`, `op` and `value` Usage: `.withLabel('region', '!=','us-east-1')`
`.withLabel(object)`	The method is overloaded, and also accepts a single object as an argument the keys `label`, `op` and `value`. Usage: `.withLabel({label: 'region', op: '!=', value: 'us-east-1'})`

Example Stream Selector Usage

logql.new()
  .withLabels({
    app: 'ecommerce',
    cluster: "primary",
    region: 'us-east-1',
  })
  .build()

// renders
{app="ecommerce", cluster="primary", region="us-east-1"}

To support a more fluent API, a single method of selector() is provided that returns an object of methods to chain from.

Operation	Description
`.eq(value)`	Generates a single label equality label selector Usage: `.selector('region').eq('us-east-1')`
`.neq(value)`	Generates a single label not equal label selector Usage: `.selector('region').neq('us-east-1')`
`.re(value)`	Generates a single label regular expression matches label selector Usage: `.selector('region').re('us-east-1')`
`.nre(value)`	Generates a single label not regular expression matches label selector Usage: `.selector('region').nre('us-east-1')`

Example Stream Selector Fluent Usage

logql.new()
  .selector('app').eq('ecommerce')
  .selector('cluster').neq('primary')
  .selector('env').re('dev|test')
  .selector('region').nre('us-east-\\d+')
  .line().eq('error')
  .json()
  .build(formatted=false)

// renders
{app="ecommerce", cluster!="primary", env=~"dev|test", region!~"us-east-\\d+"}

Log Line Filters

LogQL Line Filter Documentation

Operation	Description
`.lineEq(text)`	Filters a line to ensure that the line contains the specified text. Usage: `.lineEq('error')`
`.lineNeq(text)`	Filters a line to ensure that the line does not contains the specified text. Usage: `.lineNeq('error')`
`.lineRe(text)`	Filters a line to ensure that the line matches the specified regular expression. Usage: `.lineRe('error')`
`.lineNre(text)`	Filters a line to ensure that the line does not match the specified regular expression. Usage: `.lineNre('error')`

Example Line Filter Usage

logql.new()
  .withLabels({
    app: 'ecommerce',
    cluster: "primary",
    region: 'us-east-1',
  })
  .lineEq('error')
  .build()

// renders
{app="ecommerce", cluster="primary", region="us-east-1"}
|= `error`

To support a more fluent API, a single method of line() is provided that returns an object of methods to chain from.

Operation	Description
`.eq(value)`	Generates a single line contains filter Usage: `line().eq('error')`
`.neq(value)`	Generates a single line does not contains filter Usage: `line().neq('error')`
`.re(value)`	Generates a single line matches regular expression filter Usage: `line().re('error')`
`.nre(value)`	Generates a single line does not matchregular expression filter Usage: `line().nre('error')`

Example Line Filter Fluent Usage

logql.new()
  .selector('app').eq('ecommerce')
  .selector('cluster').eq('primary')
  .selector('env').eq('prod')
  .line().re('level=(error|warn)')
  .build()

// renders
{app="ecommerce", cluster!="primary", env="prod"}
|~ `level=(error|warn)`

Parser Expressions

LogQL Parser Documentation

Operation	Description
`.json(labels?)`	Parses the log line as `json`, optionally extracting specific values and assigning to a label. Usage: `.json()`
`.logfmt(labels?)`	Parses the log line as `logfmt`, optionally extracting specific values and assigning to a label Usage: `.logfmt()`
`.pattern(pattern)`	Parses the line using the `pattern` parser extracting values and assigning to labels Usage: `.pattern('<_> level=<level> <_>')`
`.regex(pattern)`	Parses the line using the `regex` parser extracting placeholder values and assigning to labels Usage: `.regex('.+ level=(?P<level>[^ ]+) .+')`

Example Parser Usage

logql.new()
  .selector('app').eq('ecommerce')
  .selector('cluster').eq('primary')
  .selector('env').eq('prod')
  .line().re('level=(error|warn)')
  .json()
  .build()

// renders
{app="ecommerce", cluster!="primary", env="prod"}
|~ `level=(error|warn)`
| json

Label Filter Expressions

LogQL Label Filter Documentation

Operation	Description
`.labelEq(label, value)`	Performs an equal to check on a label and value. Usage: `.labelEq('status_code', 200)`
`.labelNeq(label, value)`	Performs a not equal to check on a label and value Usage: `.labelNeq('status_code', 200)`
`.labelGt(label, value)`	Performs a greater than check on a label and value Usage: `.labelGt('status_code', 200)`
`.labelGte(label, value)`	Performs a greater than or equal to check on a label and value Usage: `.labelGte('status_code', 200)`
`.labelLt(label, value)`	Performs a less than check on a label and value Usage: `.labelLt('status_code', 200)`
`.labelLte(label, value)`	Performs a less than or equal to check on label and value Usage: `.labelLte('status_code', 200)`
`.labelRe(label, value)`	Performs a regular expression match check on a label and value Usage: `.labelRe('status_code', '2..')`
`.labelNre(label, value)`	Performs a regular expression does not match check on a label and value Usage: `.labelNre('status_code', '2..')`
`.labelNoop(label)`	Adds just the label with no check, mainly used in `drop` / `keep` Usage: `.labelNoop('status_code')`

Example Label Filter Usage

logql.new()
  .withLabels({
    app: 'ecommerce',
    cluster: 'primary',
    env: 'prod',
  })
  .lineRe('level=(error|warn)')
  .json()
  .labelNeq('status_code', 200)
  .build()

// renders
{app="ecommerce", cluster!="primary", env="prod"}
|~ `level=(error|warn)`
| json
| status_code != 200

To support a more fluent API, a single method of label(label) is provided that returns an object of methods to chain from.

Operation	Description
`.eq(value)`	Performs an equal to check on a label and value. Usage: `.label('status_code').eq(200)`
`.neq(value)`	Performs a not equal to check on a label and value Usage: `.label('status_code').neq(200)`
`.gt(value)`	Performs a greater than check on a label and value Usage: `.label('status_code').gt(200)`
`.gte(value)`	Performs a greater than or equal to check on a label and value Usage: `.label('status_code').gte(200)`
`.lt(value)`	Performs a less than check on a label and value Usage: `.label('status_code').lt(200)`
`.lte(value)`	Performs a less than or equal to check on label and value Usage: `.label('status_code').lte(200)`
`.re(value)`	Performs a regular expression match check on a label and value Usage: `.label('status_code').re('2..')`
`.nre(value)`	Performs a regular expression does not match check on a label and value Usage: `.label('status_code').nre('2..')`
`.noop()`	Adds just the label with no check, mainly used in `drop` / `keep` Usage: `.label('status_code').noop()`

Example Label Filter Fluent Usage

logql.new()
  .selector('app').eq('ecommerce')
  .selector('cluster').eq('primary')
  .selector('env').eq('dev|test')
  .line().re('level=(error|warn)')
  .json()
  .label('status_code').neq(200)
  .build()

// renders
{app="ecommerce", cluster!="primary", env="prod"}
|~ `level=(error|warn)`
| json
| status_code != 200

Logical Operator Expressions

Operation	Description
`.and(exprs[])`	Performs a logical `and` operation on the expressions Usage: `.and(['status_code == 200','response_time > 200ms'])`
`.or(exprs[])`	Performs a logical `or` operation on the expressions Usage: `.or(['status_code == 200','response_time > 200ms'])`

Example Logical Operator Usage

logql.new()
  .withLabels({
    app: 'ecommerce',
    cluster: "primary",
    region: 'us-east-1',
  })
  .json()
  .or([
    logql.new().labelEq('status_code', 500).build(),
    logql.new().labelGt('response_time', 100).build(),
    logql.new().labelGt('critical', true).build()
  ])
  .and([
    logql.new().labelEq('user', 'admin').build(),
    logql.new().labelNeq('method', 'GET').build()
  ])
  .build(formatted=false)

// renders
{app="ecommerce", cluster="primary", region="us-east-1"}
| json
| (status_code == 500 or response_time > 100 or critical > true) and (user == `admin` and method != `GET`)

Formatting Expressions

LogQL Formatting Documentation

Operation	Description
`.line_format(format)`	Rewrites the log line content by using the text/template format Usage: `.line_format('{{ .request_method }}: {{ .status_code }}')`
`.lineFormat(format)`	Wrapper for `.line_format()` Usage: `.lineFormat('{{ .request_method }}: {{ .status_code }}')`
`.label_format(label, path)`	Renames, modifies or adds labels Usage: `.label_format('response_size', '{{ .response_size \| lower }}')`
`.labelFormat(label, path)`	Wrapper for `.label_format()` Usage: `.labelFormat('response_size', '{{ .response_size \| lower }}')`
`.decolorize()`	Strips ANSI sequences (color codes) from the line Usage: `.decolorize()`
`.drop(labels[])`	Drops one or more labels or labels with a conditional check Usage: `.drop(['instance_id','host'])`
`.keep(labels[])`	Keeps one or more labels or labels with a conditional check, dropping all other labels Usage: `.keep(['status_code', 'method', 'response_size', 'path'])`

Example Label Filter Usage

logql.new()
  .selector('app').eq('ecommerce')
  .selector('cluster').eq('primary')
  .selector('env').eq('dev|test')
  .line().re('level=(error|warn)')
  .json()
  .label('status_code').neq(200)
  .decolorize()
  .line_format('{{ .request_method }}: {{ .status_code }} - {{ .path }}')
  .label_format('response_size', '{{ .response_size | lower }}')
  .keep([
    logql.new().label('instance').noop().build(),
    logql.new().label('level').noop().build(),
    logql.new().label('request_method').eq('GET').build(),
  ])
  .build()

// renders
{app="ecommerce", cluster="primary", env="dev|test"}
|~ `level=(error|warn)`
| json
| status_code != 200
| decolorize
| line_format `{{ .request_method }}: {{ .status_code }} - {{ .path }}`
| label_format response_size=`{{ .response_size | lower }}`
| keep instance, level, request_method==`GET`

Range Aggregation Expressions

LogQL Formatting Documentation

Operation	Description
`.rate(interval, resolution?, offset?)`	Calculates the number of entries per second Usage: `.rate('5m')`
`.count_over_time(interval, resolution?, offset?)`	Counts the entries for each log stream within the given range Usage: `.count_over_time('5m')`
`.countOverTime(interval, resolution?, offset?)`	Wrapper for `.count_over_time()` Usage: `.countOverTime('5m')`
`.bytes_rate(interval, resolution?, offset?)`	Calculates the number of bytes per second for each stream. Usage: `.bytes_rate('5m')`
`.bytesRate(interval, resolution?, offset?)`	Wrapper for `.bytes_rate()` Usage: `.bytesRate('5m')`
`.bytes_over_time(interval, resolution?, offset?)`	Counts the amount of bytes used by each log stream for a given range Usage: `.bytes_over_time('5m')`
`.bytesOverTime(interval, resolution?, offset?)`	Wrapper for `.bytes_over_time()` Usage: `.bytesOverTime('5m')`
`.absent_over_time(interval, resolution?, offset?)`	Returns an empty vector if the range vector passed to it has any elements and a 1-element vector with the value 1 if the range vector passed to it has no elements Usage: `.absent_over_time('5m')`
`.absentOverTime(interval, resolution?, offset?)`	Wrapper for `.absent_over_time()` Usage: `.absentOverTime('5m')`

Example Range Aggregation Usage

logql.new()
  .selector('app').eq('ecommerce')
  .selector('cluster').eq('primary')
  .selector('env').eq('dev|test')
  .line().re('level=(error|warn)')
  .count_over_time('5m')
  .build()

// renders
count_over_time(
  {app="ecommerce", cluster="primary", env="dev|test"}
  |~ `level=(error|warn)` [5m]
)

Unwrapped Range Aggregation Expressions

LogQL Formatting Documentation

Operation	Description
`.unwrap(label)`	Sets the label to unwrap for the aggregation. Unwrapped ranges uses extracted labels as sample values instead of log lines. Usage: `.unwrap('response_bytes')`
`.unwrap_duration(label)`	Unwraps the label and converts the value from a go duration format to seconds Usage: `.unwrap_duration('response_time')`
`.unwrapDuration(label)`	Wrapper for `.unwrap_duration()` Usage: `.unwrapDuration('response_time')`
`.unwrap_bytes(label)`	Unwraps the label and converts the value from a bytes unit to bytes Usage: `.unwrap_bytes('response_size')`
`.unwrapBytes(label)`	Wrapper for `.unwrap_bytes()` Usage: `.unwrapBytes('response_size')`
`.avg_over_time(interval, resolution?, offset?, by?, without?)`	Calculates the average value of all points in the specified interval. Usage: `.avg_over_time('5m')`
`.avg_over_time_by(interval, by, resolution?, offset?)`	Wrapper for `avg_over_time()` Usage: `.avg_over_time_by('5m', ['region', 'cluster'])`
`.avg_over_time_without(interval, without, resolution?, offset?)`	Wrapper for `avg_over_time()` Usage: `.avg_over_time_without('5m', ['region', 'cluster'])`
`.avgOverTime(interval, resolution?, offset?, by?, without?)`	Wrapper for `avg_over_time()` Usage: `.avgOverTime('5m')`
`.avgOverTimeBy(interval, by, resolution?, offset?)`	Wrapper for `avg_over_time()` Usage: `.avgOverTimeBy('5m', ['region', 'cluster'])`
`.avgOverTimeWithout(interval, without, resolution?, offset?)`	Wrapper for `avg_over_time()` Usage: `.avgOverTimeWithout('5m', ['region', 'cluster'])`
`.min_over_time(interval, resolution?, offset?, by?, without?)`	Calculates the minimum value of all points in the specified interval Usage: `.min_over_time('5m')`
`.min_over_time_by(interval, by, resolution?, offset?)`	Wrapper for `min_over_time()` Usage: `.min_over_time_by('5m', ['region', 'cluster'])`
`.min_over_time_without(interval, without, resolution?, offset?)`	Wrapper for `min_over_time()` Usage: `.min_over_time_without('5m', ['region', 'cluster'])`
`.minOverTime(interval, resolution?, offset?, by?, without?)`	Wrapper for `min_over_time()` Usage: `.minOverTime('5m')`
`.minOverTimeBy(interval, by, resolution?, offset?)`	Wrapper for `min_over_time()` Usage: `.minOverTimeBy('5m', ['region', 'cluster'])`
`.minOverTimeWithout(interval, without, resolution?, offset?)`	Wrapper for `min_over_time()` Usage: `.minOverTimeWithout('5m', ['region', 'cluster'])`
`.max_over_time(interval, resolution?, offset?, by?, without?)`	Calculates the maximum value of all points in the specified interval. Usage: `.max_over_time('5m')`
`.max_over_time_by(interval, by, resolution?, offset?)`	Wrapper for `max_over_time()` Usage: `.max_over_time_by('5m', ['region', 'cluster'])`
`.max_over_time_without(interval, without, resolution?, offset?)`	Wrapper for `max_over_time()` Usage: `.max_over_time_without('5m', ['region', 'cluster'])`
`.maxOverTime(interval, resolution?, offset?, by?, without?)`	Wrapper for `max_over_time()` Usage: `.maxOverTime('5m')`
`.maxOverTimeBy(interval, by, resolution?, offset?)`	Wrapper for `max_over_time()` Usage: `.maxOverTimeBy('5m', ['region', 'cluster'])`
`.maxOverTimeWithout(interval, without, resolution?, offset?)`	Wrapper for `max_over_time()` Usage: `.maxOverTimeWithout('5m', ['region', 'cluster'])`
`.first_over_time(interval, resolution?, offset?, by?, without?)`	Calculates the first value of all points in the specified interval Usage: `.first_over_time('5m')`
`.first_over_time_by(interval, by, resolution?, offset?)`	Wrapper for `first_over_time()` Usage: `.first_over_time_by('5m', ['region', 'cluster'])`
`.first_over_time_without(interval, without, resolution?, offset?)`	Wrapper for `first_over_time()` Usage: `.first_over_time_without('5m', ['region', 'cluster'])`
`.firstOverTime(interval, resolution?, offset?, by?, without?)`	Wrapper for `first_over_time()` Usage: `.firstOverTime('5m')`
`.firstOverTimeBy(interval, by, resolution?, offset?)`	Wrapper for `first_over_time()` Usage: `.firstOverTimeBy('5m', ['region', 'cluster'])`
`.firstOverTimeWithout(interval, without, resolution?, offset?)`	Wrapper for `first_over_time()` Usage: `.firstOverTimeWithout('5m', ['region', 'cluster'])`
`.last_over_time(interval, resolution?, offset?, by?, without?)`	Calculates the last value of all points in the specified interval Usage: `.last_over_time('5m')`
`.last_over_time_by(interval, by, resolution?, offset?)`	Wrapper for `last_over_time()` Usage: `.last_over_time_by('5m', ['region', 'cluster'])`
`.last_over_time_without(interval, without, resolution?, offset?)`	Wrapper for `last_over_time()` Usage: `.last_over_time_without('5m', ['region', 'cluster'])`
`.lastOverTime(interval, resolution?, offset?, by?, without?)`	Wrapper for `last_over_time()` Usage: `.lastOverTime('5m')`
`.lastOverTimeBy(interval, by, resolution?, offset?)`	Wrapper for `last_over_time()` Usage: `.lastOverTimeBy('5m', ['region', 'cluster'])`
`.lastOverTimeWithout(interval, without, resolution?, offset?)`	Wrapper for `last_over_time()` Usage: `.lastOverTimeWithout('5m', ['region', 'cluster'])`
`.rate_counter(interval, resolution?, offset?)`	Calculates per second rate of the values in the specified interval and treating them as "counter metric" Usage: `.rate_counter('5m')`
`.rateCounter(interval, resolution?, offset?)`	c Usage: `.rate_counter('5m')`
`.stdvar_over_time(interval, resolution?, offset?, by?, without?)`	Calculates the population standard variance of the values in the specified interval. Usage: `.stdvar_over_time('5m', ['region', 'cluster'])`
Wrapper for `stdvar_over_time()`
`.stdvar_over_time_without(interval, without, resolution?, offset?)`	Wrapper for `stdvar_over_time()` Usage: `.stdvar_over_time_without('5m', ['region', 'cluster'])`
`.stdvarOverTime(interval, resolution?, offset?, by?, without?)`	Wrapper for `stdvar_over_time()` Usage: `.stdvarOverTime('5m')`
`.stdvarOverTimeBy(interval, by, resolution?, offset?)`	Wrapper for `stdvar_over_time()` Usage: `.stdvarOverTimeBy('5m', ['region', 'cluster'])`
`.stdvarOverTimeWithout(interval, without, resolution?, offset?)`	Wrapper for `stdvar_over_time()` Usage: `.stdvarOverTimeWithout('5m', ['region', 'cluster'])`
`.stddev_over_time(interval, resolution?, offset?, by?, without?)`	Calculates the population standard deviation of the values in the specified interval. Usage: `.stddev_over_time('5m')`
`.stddev_over_time_by(interval, by, resolution?, offset?)`	Wrapper for `stddev_over_time()` Usage: `.stddev_over_time_by('5m', ['region', 'cluster'])`
`.stddev_over_time_without(interval, without, resolution?, offset?)`	Wrapper for `stddev_over_time()` Usage: `.stddev_over_time_without('5m', ['region', 'cluster'])`
`.stddevOverTime(interval, resolution?, offset?, by?, without?)`	Wrapper for `stddev_over_time()` Usage: `.stddevOverTime('5m')`
`.stddevOverTimeBy(interval, by, resolution?, offset?)`	Wrapper for `stddev_over_time()` Usage: `.stddevOverTimeBy('5m', ['region', 'cluster'])`
`.stddevOverTimeWithout(interval, without, resolution?, offset?)`	Wrapper for `stddev_over_time()` Usage: `.stddevOverTimeWithout('5m', ['region', 'cluster'])`
`.quantile_over_time(quantile, interval, resolution?, offset?, by?, without?)`	Calculates the φ-quantile (0 ≤ φ ≤ 1) of the values in the specified interval. Usage: `.quantile_over_time('0.95', '5m')`
`.quantile_over_time_by(quantile, interval, by, resolution?, offset?)`	Wrapper for `quantile_over_time()` Usage: `.quantile_over_time_by('0.95', '5m', ['region', 'cluster'])`
`.quantile_over_time_without(quantile, interval, without, resolution?, offset?)`	Wrapper for `quantile_over_time()` Usage: `.quantile_over_time_without('0.95', '5m', ['region', 'cluster'])`
`.quantileOverTime(quantile, interval, resolution?, offset?, by?, without?)`	Wrapper for `quantile_over_time()` Usage: `.quantileOverTime('0.95', '5m')`
`.quantileOverTimeBy(quantile, interval, by, resolution?, offset?)`	Wrapper for `quantile_over_time()` Usage: `.quantileOverTimeBy('0.95', '5m', ['region', 'cluster'])`
`.quantileOverTimeWithout(quantile, interval, without, resolution?, offset?)`	Wrapper for `quantile_over_time()` Usage: `.quantileOverTimeWithout('0.95', '5m', ['region', 'cluster'])`

Example Unwrapped Range Aggregation Usage

logql.new()
  .selector('app').eq('ecommerce')
  .selector('cluster').eq('primary')
  .selector('env').eq('dev|test')
  .line().re('level=(error|warn)')
  .count_over_time('5m')
  .build()

// renders
avg_over_time(
  {app="ecommerce", cluster="primary", region="us-east-1"}
  |= `error`
  | logfmt
  | unwrap bytes(response_size) [1h:5m]
) by (cluster, region)

Aggregation Expressions

LogQL Formatting Documentation

Operation	Description
`.sum(by?, without?)`	Calculate sum over labels Usage: `.sum()`
`.sum_by(by)`	Wrapper for `sum(by=[])` Usage: `.sumBy(['cluster', 'region'])`
`.sumBy(by)`	Wrapper for `sum(by=[])` Usage: `.sumBy(['cluster', 'region'])`
`.sum_without(without)`	Wrapper for `sum(without=[])` Usage: `.sumWithout(['cluster', 'region')`
`.sumWithout(without)`	Wrapper for `sum(without=[])` Usage: `.sumWithout(['cluster', 'region')`
`.avg(by?, without?)`	Calculate the average over labels Usage: `.avg()`
`.avg_by(by)`	Wrapper for `avg(by=[])` Usage: `.avgBy(['cluster', 'region')`
`.avgBy(by)`	Wrapper for `avg(by=[])` Usage: `.avgBy(['cluster', 'region')`
`.avg_without(without)`	Wrapper for `avg(without=[])` Usage: `.avgWithout(['cluster', 'region')`
`.avgWithout(without)`	Wrapper for `avg(without=[])` Usage: `.avgWithout(['cluster', 'region')`
`.min(by?, without?)`	Select minimum over labels Usage: `.min()`
`.min_by(by)`	Wrapper for `min(by=[])` Usage: `.minBy(['cluster', 'region')`
`.minBy(by)`	Wrapper for `min(by=[])` Usage: `.minBy(['cluster', 'region')`
`.min_without(without)`	Wrapper for `min(without=[])` Usage: `.minWithout(['cluster', 'region')`
`.minWithout(without)`	Wrapper for `min(without=[])` Usage: `.minWithout(['cluster', 'region')`
`.max(by?, without?)`	Select maximum over labels Usage: `.max()`
`.max_by(by)`	Wrapper for `max(by=[])` Usage: `.maxBy(['cluster', 'region')`
`.maxBy(by)`	Wrapper for `max(by=[])` Usage: `.maxBy(['cluster', 'region')`
`.max_without(without)`	Wrapper for `max(without=[])` Usage: `.maxWithout(['cluster', 'region')`
`.maxWithout(without)`	Wrapper for `max(without=[])` Usage: `.maxWithout(['cluster', 'region')`
`.stddev(by?, without?)`	Calculate the population standard deviation over labels Usage: `.stddev()`
`.stddev_by(by)`	Wrapper for `stddev(by=[])` Usage: `.stddevBy(['cluster', 'region')`
`.stddevBy(by)`	Wrapper for `stddev(by=[])` Usage: `.stddevBy(['cluster', 'region')`
`.stddev_without(without)`	Wrapper for `stddev(without=[])` Usage: `.stddevWithout(['cluster', 'region')`
`.stddevWithout(without)`	Wrapper for `stddev(without=[])` Usage: `.stddevWithout(['cluster', 'region')`
`.stdvar(by?, without?)`	Calculate the population standard variance over labels Usage: `.stdvar()`
`.stdvar_by(by)`	Wrapper for `stdvar(by=[])` Usage: `.stdvarBy(['cluster', 'region')`
`.stdvarBy(by)`	Wrapper for `stdvar(by=[])` Usage: `.stdvarBy(['cluster', 'region')`
`.stdvar_without(without)`	Wrapper for `stdvar(without=[])` Usage: `.stdvarWithout(['cluster', 'region')`
`.stdvarWithout(without)`	Wrapper for `stdvar(without=[])` Usage: `.stdvarWithout(['cluster', 'region')`
`.count(by?, without?)`	Count number of elements in the vector Usage: `.count()`
`.count_by(by)`	Wrapper for `count(by=[])` Usage: `.countBy(['cluster', 'region')`
`.countBy(by)`	Wrapper for `count(by=[])` Usage: `.countBy(['cluster', 'region')`
`.count_without(without)`	Wrapper for `count(without=[])` Usage: `.countWithout(['cluster', 'region')`
`.countWithout(without)`	Wrapper for `count(without=[])` Usage: `.countWithout(['cluster', 'region')`
`.topk(k, by?, without?)`	Select largest k elements by sample value Usage: `.topk(10)`
`.topk_by(k, by)`	Wrapper for `.topk(k, by=[])` Usage: `.topk_by(10, ['region'])`
`.topkBy(k, by)`	Wrapper for `.topk(k, by=[])` Usage: `.topkBy(10, ['region'])`
`.topk_without(k, without)`	Wrapper for `.topk(k, without=[])` Usage: `.topk_without(10, ['region'])`
`.topkWithout(k, without)`	Wrapper for `.topk(k, without=[])` Usage: `.topkWithout(10, ['region'])`
`.bottomk(k, by?, without?)`	Select smallest k elements by sample value Usage: `.bottomk(10)`
`.bottomk_by(k, by)`	Wrapper for `.bottomk(k, by=[])` Usage: `.bottomk_by(10, ['region'])`
`.bottomkBy(k, by)`	Wrapper for `.bottomk(k, by=[])` Usage: `.bottomkBy(10, ['region'])`
`.bottomk_without(k, without)`	Wrapper for `.bottomk(k, without=[])` Usage: `.bottomk_without(10, ['region'])`
`.bottomkWithout(k, without)`	Wrapper for `.bottomk(k, without=[])` Usage: `.bottomkWithout(10, ['region'])`
`.sort(by?, without?)`	returns vector elements sorted by their sample values, in ascending order. Usage: `.sort()`
`.sort_by(by)`	Wrapper for `.sort(by=[])` Usage: `.sort_by(['region', 'cluster'])`
`.sortBy(by)`	Wrapper for `.sort(by=[])` Usage: `.sortBy(['region', 'cluster'])`
`.sort_without(without)`	`.sort(without=[])` Usage: `.sort_without(['region', 'cluster'])`
`.sortWithout(without)`	`.sort(without=[])` Usage: `.sortWithout(['region', 'cluster'])`
`.sort_desc(by?, without?)`	Same as sort, but sorts in descending order. Usage: `.sort_desc()`
`.sortDesc(by?, without?)`	Wrapper for `.sort_desc()` Usage: `.sortDesc()`
`.sort_desc_by(by)`	Wrapper for `.sort_desc(by=[])` Usage: `.sort_desc_by(['region', 'cluster'])`
`.sortDescBy(by)`	Wrapper for `.sort_desc(by=[])` Usage: `sortDescBy(['region', 'cluster'])`
`.sort_desc_without(without)`	Wrapper for `.sort_desc(without=[])` Usage: `.sort_desc_without(['region', 'cluster'])`
`.sortDescWithout(without)`	Wrapper for `.sort_desc(without=[])` Usage: `.sortDescWithout(['region', 'cluster'])`

Example Aggregations Usage

logql.new()
  .selector('app').eq('ecommerce')
  .selector('cluster').eq('primary')
  .selector('env').eq('dev|test')
  .line().re('level=(error|warn)')
  .count_over_time('5m')
  .build()

// renders
sort_desc(
  sum by (region, cluster) (
    count_over_time(
      {app="ecommerce", cluster="primary", region="us-east-1"} |= `error` [1m]
    )
  )
)

Name		Name	Last commit message	Last commit date
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.github		.github
tests		tests
tools		tools
.editorconfig		.editorconfig
.gitignore		.gitignore
.markdownlint.yaml		.markdownlint.yaml
.pre-commit-config.yaml		.pre-commit-config.yaml
.shellcheckrc		.shellcheckrc
.textlintrc		.textlintrc
.yamllint		.yamllint
CODEOWNERS		CODEOWNERS
LICENSE		LICENSE
Makefile		Makefile
Pipfile		Pipfile
Pipfile.lock		Pipfile.lock
README.md		README.md
logql.libsonnet		logql.libsonnet
package.json		package.json
unit-tests.jsonnet		unit-tests.jsonnet
yarn.lock		yarn.lock

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logql-jsonnet

API Reference and LogQL Support

Log Stream Selectors

Example Stream Selector Usage

Example Stream Selector Fluent Usage

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API Reference and LogQL Support

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Example Line Filter Fluent Usage

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Example Parser Usage

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Example Label Filter Usage

Example Label Filter Fluent Usage

Logical Operator Expressions

Example Logical Operator Usage

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