TS.NRANGE

is the number of time series keys that follow. It must be a positive integer and must equal the number of key arguments.

are the explicit time series keys to query. Key order and duplicate keys are significant: the reply has one value per key argument, in the order the keys are given. A repeated key produces a separate value for each occurrence.

is the start timestamp for the range query (integer Unix timestamp in milliseconds) or - to denote the timestamp of the earliest sample among the specified time series. The range is inclusive.

is the end timestamp for the range query (integer Unix timestamp in milliseconds) or + to denote the timestamp of the latest sample among the specified time series. The range is inclusive.

is used when a time series is a compaction. With LATEST, TS.NRANGE 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.NRANGE 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 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]. Samples are filtered before pivoting.

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

filters samples by minimum and maximum values. A sample passes the filter if its value is within the inclusive range [min, max]. min and max cannot be NaN values. Samples are filtered before pivoting.

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

limits the number of returned entries, keeping those with the lowest timestamps. The limit is applied after the per-timestamp merge.

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.

align 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.

aggregates samples into time buckets.

Provide one or more aggregators per key, as a separate argument in the same order as the keys: the first argument applies to the first key, and so on. The number of these arguments must equal numkeys, and all keys share the same bucketDuration.

Each per-key argument is either a single aggregator or a comma-separated list of aggregators (for example avg,max), exactly as in TS.RANGE; no whitespace is allowed. A key contributes one value for each aggregator you list for it, and a key's values appear together in the reply, in the order you list its aggregators. To compute several aggregations for one series, give that key a comma-separated list such as min,max.

• each aggregator is one of the following:

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

• bucketDuration is the 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. When a bucket is reported for one key but a different key has no data in that bucket, that key's value is NaN.

Create two time series and add samples at partially overlapping timestamps.

127.0.0.1:6379> TS.CREATE {sensor}:1
OK
127.0.0.1:6379> TS.CREATE {sensor}:2
OK
127.0.0.1:6379> TS.MADD {sensor}:1 1000 10 {sensor}:1 2000 12
1) (integer) 1000
2) (integer) 2000
127.0.0.1:6379> TS.MADD {sensor}:2 1000 20 {sensor}:2 3000 25
1) (integer) 1000
2) (integer) 3000

Query both series and group the samples by timestamp. One entry is returned for every distinct timestamp produced by at least one key, with the values ordered by input key. A key with no sample at that timestamp has a NaN value.

127.0.0.1:6379> TS.NRANGE 2 {sensor}:1 {sensor}:2 - +
1) 1) (integer) 1000
   2) 1) 10
      2) 20
2) 1) (integer) 2000
   2) 1) 12
      2) NaN
3) 1) (integer) 3000
   2) 1) NaN
      2) 25

In aggregation mode, supply one aggregator per key, in key order. Here {sensor}:1 is aggregated with avg and {sensor}:2 with sum, both over 1000-millisecond buckets.

127.0.0.1:6379> TS.MADD {sensor}:1 1000 10 {sensor}:1 1100 20 {sensor}:1 2000 30
1) (integer) 1000
2) (integer) 1100
3) (integer) 2000
127.0.0.1:6379> TS.MADD {sensor}:2 1000 5 {sensor}:2 1100 15 {sensor}:2 2000 25
1) (integer) 1000
2) (integer) 1100
3) (integer) 2000
127.0.0.1:6379> TS.NRANGE 2 {sensor}:1 {sensor}:2 - + AGGREGATION avg sum 1000
1) 1) (integer) 1000
   2) 1) 15
      2) 20
2) 1) (integer) 2000
   2) 1) 30
      2) 25

To compute several aggregators for a single key, pass them as a comma-separated list. Here {sensor}:1 uses avg,max (two values) and {sensor}:2 uses sum (one value). Each timestamp's values are a single flat list: {sensor}:1's avg, then its max, then {sensor}:2's sum.

127.0.0.1:6379> TS.NRANGE 2 {sensor}:1 {sensor}:2 - + AGGREGATION avg,max sum 1000
1) 1) (integer) 1000
   2) 1) 15
      2) 20
      3) 20
2) 1) (integer) 2000
   2) 1) 30
      2) 30
      3) 25