TS.MRANGE

is the start timestamp for the range query (integer Unix timestamp in milliseconds) or - to denote the timestamp of the earliest sample among all the time series that passes FILTER filterExpr....

is the end timestamp for the range query (integer Unix timestamp in milliseconds) or + to denote the timestamp of the latest sample among all the time series that passes FILTER filterExpr....

filters time series based on their labels and label values. Each filter expression has one of the following syntaxes:

• label!= - the time series has a label named label
• label=value - the time series has a label named label with a value equal to value
• label=(value1,value2,...) - the time series has a label named label with a value equal to one of the values in the list
• label= - the time series does not have a label named label
• label!=value - the time series does not have a label named label with a value equal to value
• label!=(value1,value2,...) - the time series does not have a label named label with a value equal to any of the values in the list

Notes:

• At least one filter expression with a syntax label=value or label=(value1,value2,...) is required.
• Filter expressions are conjunctive. For example, the filter type=temperature room=study means that a time series is a temperature time series of a study room.
• Whitespaces are unallowed in a filter expression except between quotes or double quotes in values - e.g., x="y y" or x='(y y,z z)'.

is used when a time series is a compaction. With LATEST, TS.MRANGE also reports the compacted value of the latest (possibly partial) bucket, given that this bucket's start time falls within [fromTimestamp, toTimestamp]. Without LATEST, TS.MRANGE does not report the latest (possibly partial) bucket. When a time series is not a compaction, LATEST is ignored.

The data in the latest bucket of a compaction is possibly partial. A bucket is closed and compacted only upon the arrival of a new sample that opens a new latest bucket. There are cases, however, when the compacted value of the latest (possibly partial) bucket is also required. In such a case, use LATEST.

filters samples by a list of specific timestamps. A sample passes the filter if its exact timestamp is specified and falls within [fromTimestamp, toTimestamp].

When used together with AGGREGATION: samples are filtered before being aggregated.

filters samples by minimum and maximum values.

When used together with AGGREGATION: samples are filtered before being aggregated.

includes in the reply all label-value pairs representing metadata labels of the time series. If WITHLABELS or SELECTED_LABELS are not specified, by default, an empty list is reported as label-value pairs.

returns a subset of the label-value pairs that represent metadata labels of the time series. Use when a large number of labels exists per series, but only the values of some of the labels are required. If WITHLABELS or SELECTED_LABELS are not specified, by default, an empty list is reported as label-value pairs.

When used without AGGREGATION: limits the number of reported samples per time series.

When used together with AGGREGATION: limits the number of reported buckets.

is a time bucket alignment control for AGGREGATION. It controls the time bucket timestamps by changing the reference timestamp on which a bucket is defined.

Values include:

• start or -: The reference timestamp will be the query start interval time (fromTimestamp) which can't be -
• end or +: The reference timestamp will be the query end interval time (toTimestamp) which can't be +
• A specific timestamp: align the reference timestamp to a specific time

Note: When not provided, alignment is set to 0.

per time series, aggregates samples into time buckets, where:

• aggregator takes one of the following aggregation types:

  aggregator	Description
  avg	Arithmetic mean of all values
  sum	Sum of all values
  min	Minimum value
  max	Maximum value
  range	Difference between maximum value and minimum value
  count	Number of values
  first	Value with lowest timestamp in the bucket
  last	Value with highest timestamp in the bucket
  std.p	Population standard deviation of the values
  std.s	Sample standard deviation of the values
  var.p	Population variance of the values
  var.s	Sample variance of the values
  twa	Time-weighted average over the bucket's timeframe (since RedisTimeSeries v1.8)

• bucketDuration is duration of each bucket, in milliseconds.

Without ALIGN, bucket start times are multiples of bucketDuration.

With ALIGN align, bucket start times are multiples of bucketDuration with remainder align % bucketDuration.

The first bucket start time is less than or equal to fromTimestamp.

controls how bucket timestamps are reported.

is a flag, which, when specified, reports aggregations also for empty buckets.

Regardless of the values of fromTimestamp and toTimestamp, no data is reported for buckets that end before the earliest sample or begin after the latest sample in the time series.

splits time series into groups, each group contains time series that share the same value for the provided label name, then aggregates results in each group.

When combined with AGGREGATION the GROUPBY/REDUCE is applied post aggregation stage.

• label is label name. A group is created for all time series that share the same value for this label.

• reducer is an aggregation type used to aggregate the results in each group.

  reducer	Description
  avg	Arithmetic mean of all non-NaN values (since RedisTimeSeries v1.8)
  sum	Sum of all non-NaN values
  min	Minimum non-NaN value
  max	Maximum non-NaN value
  range	Difference between maximum non-NaN value and minimum non-NaN value (since RedisTimeSeries v1.8)
  count	Number of non-NaN values (since RedisTimeSeries v1.8)
  std.p	Population standard deviation of all non-NaN values (since RedisTimeSeries v1.8)
  std.s	Sample standard deviation of all non-NaN values (since RedisTimeSeries v1.8)
  var.p	Population variance of all non-NaN values (since RedisTimeSeries v1.8)
  var.s	Sample variance of all non-NaN values (since RedisTimeSeries v1.8)

Notes:

• The produced time series is named <label>=<value>
• The produced time series contains two labels with these label array structures:
  • __reducer__, the reducer used (e.g., "count")
  • __source__, the list of time series keys used to compute the grouped series (e.g., "key1,key2,key3")

Create two stocks and add their prices at three different timestamps.

127.0.0.1:6379> TS.CREATE stock:A LABELS type stock name A
OK
127.0.0.1:6379> TS.CREATE stock:B LABELS type stock name B
OK
127.0.0.1:6379> TS.MADD stock:A 1000 100 stock:A 1010 110 stock:A 1020 120
1) (integer) 1000
2) (integer) 1010
3) (integer) 1020
127.0.0.1:6379> TS.MADD stock:B 1000 120 stock:B 1010 110 stock:B 1020 100
1) (integer) 1000
2) (integer) 1010
3) (integer) 1020

You can now retrieve the maximum stock price per timestamp.

127.0.0.1:6379> TS.MRANGE - + WITHLABELS FILTER type=stock GROUPBY type REDUCE max
1) 1) "type=stock"
   2) 1) 1) "type"
         2) "stock"
      2) 1) "__reducer__"
         2) "max"
      3) 1) "__source__"
         2) "stock:A,stock:B"
   3) 1) 1) (integer) 1000
         2) 120
      2) 1) (integer) 1010
         2) 110
      3) 1) (integer) 1020
         2) 120

The FILTER type=stock clause returns a single time series representing stock prices. The GROUPBY type REDUCE max clause splits the time series into groups with identical type values, and then, for each timestamp, aggregates all series that share the same type value using the max aggregator.

Create two stocks and add their prices at nine different timestamps.

127.0.0.1:6379> TS.CREATE stock:A LABELS type stock name A
OK
127.0.0.1:6379> TS.CREATE stock:B LABELS type stock name B
OK
127.0.0.1:6379> TS.MADD stock:A 1000 100 stock:A 1010 110 stock:A 1020 120
1) (integer) 1000
2) (integer) 1010
3) (integer) 1020
127.0.0.1:6379> TS.MADD stock:B 1000 120 stock:B 1010 110 stock:B 1020 100
1) (integer) 1000
2) (integer) 1010
3) (integer) 1020
127.0.0.1:6379> TS.MADD stock:A 2000 200 stock:A 2010 210 stock:A 2020 220
1) (integer) 2000
2) (integer) 2010
3) (integer) 2020
127.0.0.1:6379> TS.MADD stock:B 2000 220 stock:B 2010 210 stock:B 2020 200
1) (integer) 2000
2) (integer) 2010
3) (integer) 2020
127.0.0.1:6379> TS.MADD stock:A 3000 300 stock:A 3010 310 stock:A 3020 320
1) (integer) 3000
2) (integer) 3010
3) (integer) 3020
127.0.0.1:6379> TS.MADD stock:B 3000 320 stock:B 3010 310 stock:B 3020 300
1) (integer) 3000
2) (integer) 3010
3) (integer) 3020

Now, for each stock, calculate the average stock price per a 1000-millisecond timeframe, and then retrieve the stock with the maximum average for that timeframe.

127.0.0.1:6379> TS.MRANGE - + WITHLABELS AGGREGATION avg 1000 FILTER type=stock GROUPBY type REDUCE max
1) 1) "type=stock"
   2) 1) 1) "type"
         2) "stock"
      2) 1) "__reducer__"
         2) "max"
      3) 1) "__source__"
         2) "stock:A,stock:B"
   3) 1) 1) (integer) 1000
         2) 110
      2) 1) (integer) 2000
         2) 210
      3) 1) (integer) 3000
         2) 310

Query all time series with the metric label equal to cpu, then group the time series by the value of their metric_name label value and for each group return the maximum value and the time series keys (source) with that value.

127.0.0.1:6379> TS.ADD ts1 1548149180000 90 labels metric cpu metric_name system
(integer) 1548149180000
127.0.0.1:6379> TS.ADD ts1 1548149185000 45
(integer) 1548149185000
127.0.0.1:6379> TS.ADD ts2 1548149180000 99 labels metric cpu metric_name user
(integer) 1548149180000
127.0.0.1:6379> TS.MRANGE - + WITHLABELS FILTER metric=cpu GROUPBY metric_name REDUCE max
1) 1) "metric_name=system"
   2) 1) 1) "metric_name"
         2) "system"
      2) 1) "__reducer__"
         2) "max"
      3) 1) "__source__"
         2) "ts1"
   3) 1) 1) (integer) 1548149180000
         2) 90
      2) 1) (integer) 1548149185000
         2) 45
2) 1) "metric_name=user"
   2) 1) 1) "metric_name"
         2) "user"
      2) 1) "__reducer__"
         2) "max"
      3) 1) "__source__"
         2) "ts2"
   3) 1) 1) (integer) 1548149180000
         2) 99

Query all time series with the metric label equal to cpu, then filter values larger or equal to 90.0 and smaller or equal to 100.0.

127.0.0.1:6379> TS.ADD ts1 1548149180000 90 labels metric cpu metric_name system
(integer) 1548149180000
127.0.0.1:6379> TS.ADD ts1 1548149185000 45
(integer) 1548149185000
127.0.0.1:6379> TS.ADD ts2 1548149180000 99 labels metric cpu metric_name user
(integer) 1548149180000
127.0.0.1:6379> TS.MRANGE - + FILTER_BY_VALUE 90 100 WITHLABELS FILTER metric=cpu
1) 1) "ts1"
   2) 1) 1) "metric"
         2) "cpu"
      2) 1) "metric_name"
         2) "system"
   3) 1) 1) (integer) 1548149180000
         2) 90
2) 1) "ts2"
   2) 1) 1) "metric"
         2) "cpu"
      2) 1) "metric_name"
         2) "user"
   3) 1) 1) (integer) 1548149180000
         2) 99

Query all time series with the metric label equal to cpu, but only return the team label.

127.0.0.1:6379> TS.ADD ts1 1548149180000 90 labels metric cpu metric_name system team NY
(integer) 1548149180000
127.0.0.1:6379> TS.ADD ts1 1548149185000 45
(integer) 1548149185000
127.0.0.1:6379> TS.ADD ts2 1548149180000 99 labels metric cpu metric_name user team SF
(integer) 1548149180000
127.0.0.1:6379> TS.MRANGE - + SELECTED_LABELS team FILTER metric=cpu
1) 1) "ts1"
   2) 1) 1) "team"
         2) "NY"
   3) 1) 1) (integer) 1548149180000
         2) 90
      2) 1) (integer) 1548149185000
         2) 45
2) 1) "ts2"
   2) 1) 1) "team"
         2) "SF"
   3) 1) 1) (integer) 1548149180000
         2) 99