# @redis/time-series

This package provides support for the [RedisTimeSeries](https://redis.io/docs/data-types/timeseries/) module, which adds a time series data structure to Redis.

Should be used with [`redis`/`@redis/client`](https://github.com/redis/node-redis).

:warning: To use these extra commands, your Redis server must have the RedisTimeSeries module installed.

## Usage

For a complete example, see [`time-series.js`](https://github.com/redis/node-redis/blob/master/examples/time-series.js) in the Node Redis examples folder.

### Creating Time Series data structure in Redis

The [`TS.CREATE`](https://oss.redis.com/redistimeseries/commands/#tscreate) command creates a new time series.

Here, we'll create a new time series "`temperature`":

```javascript

import { createClient } from 'redis';
import { TimeSeriesDuplicatePolicies, TimeSeriesEncoding, TIME_SERIES_AGGREGATION_TYPE } from '@redis/time-series';

...
const created = await client.ts.create('temperature', {
  RETENTION: 86400000, // 1 day in milliseconds
  ENCODING: TimeSeriesEncoding.UNCOMPRESSED, // No compression - When not specified, the option is set to COMPRESSED
  DUPLICATE_POLICY: TimeSeriesDuplicatePolicies.BLOCK, // No duplicates - When not specified: set to the global DUPLICATE_POLICY configuration of the database (which by default, is BLOCK).
});

if (created === 'OK') {
  console.log('Created timeseries.');
} else {
  console.log('Error creating timeseries :(');
  process.exit(1);
}
```

### Adding new value to a Time Series data structure in Redis

With RedisTimeSeries, we can add a single value to time series data structure using the [`TS.ADD`](https://redis.io/commands/ts.add/) command and if we would like to add multiple values we can use the [`TS.MADD`](https://redis.io/commands/ts.madd/) command.

```javascript

let value = Math.floor(Math.random() * 1000) + 1; // Random data point value
let currentTimestamp = 1640995200000; // Jan 1 2022 00:00:00
let num = 0;

while (num < 10000) {
  // Add a new value to the timeseries, providing our own timestamp:
  // https://redis.io/commands/ts.add/
  await client.ts.add('temperature', currentTimestamp, value);
  console.log(`Added timestamp ${currentTimestamp}, value ${value}.`);

  num += 1;
  value = Math.floor(Math.random() * 1000) + 1; // Get another random value
  currentTimestamp += 1000; // Move on one second.
}

// Add multiple values to the timeseries in round trip to the server:
// https://redis.io/commands/ts.madd/
const response = await client.ts.mAdd([{
  key: 'temperature',
  timestamp: currentTimestamp + 60000,
  value: Math.floor(Math.random() * 1000) + 1
}, {
  key: 'temperature',
  timestamp: currentTimestamp + 120000,
  value: Math.floor(Math.random() * 1000) + 1
}]);
```

### Retrieving Time Series data from Redis

With RedisTimeSeries, we can retrieve the time series data using the [`TS.RANGE`](https://redis.io/commands/ts.range/) command by passing the criteria as follows:

```javascript
// Query the timeseries with TS.RANGE:
// https://redis.io/commands/ts.range/
const fromTimestamp = 1640995200000; // Jan 1 2022 00:00:00
const toTimestamp = 1640995260000; // Jan 1 2022 00:01:00
const rangeResponse = await client.ts.range('temperature', fromTimestamp, toTimestamp, {
  // Group into 10 second averages.
  AGGREGATION: {
    type: TIME_SERIES_AGGREGATION_TYPE.AVG,
    timeBucket: 10000
  }
});

console.log('RANGE RESPONSE:');
// rangeResponse looks like:
// [
//   { timestamp: 1640995200000, value: 356.8 },
//   { timestamp: 1640995210000, value: 534.8 },
//   { timestamp: 1640995220000, value: 481.3 },
//   { timestamp: 1640995230000, value: 437 },
//   { timestamp: 1640995240000, value: 507.3 },
//   { timestamp: 1640995250000, value: 581.2 },
//   { timestamp: 1640995260000, value: 600 }
// ]
```

For multiple aggregations in one command, use the dedicated `*MultiAggr` methods:

```javascript
const multiRangeResponse = await client.ts.rangeMultiAggr('temperature', fromTimestamp, toTimestamp, {
  AGGREGATION: {
    types: [
      TIME_SERIES_AGGREGATION_TYPE.MIN,
      TIME_SERIES_AGGREGATION_TYPE.MAX,
      TIME_SERIES_AGGREGATION_TYPE.AVG
    ],
    timeBucket: 10000
  }
});

// multiRangeResponse looks like:
// [
//   { timestamp: 1640995200000, values: [120, 580, 356.8] },
//   ...
// ]
```

Equivalent multi-aggregation helpers are also available for reverse and multi-key variants:
`revRangeMultiAggr`, `mRangeMultiAggr`, `mRevRangeMultiAggr`, `mRangeWithLabelsMultiAggr`,
`mRevRangeWithLabelsMultiAggr`, `mRangeSelectedLabelsMultiAggr`, and `mRevRangeSelectedLabelsMultiAggr`.

### Altering Time Series data Stored in Redis

RedisTimeSeries includes commands that can update values in a time series data structure.

Using the [`TS.ALTER`](https://redis.io/commands/ts.alter/) command, we can update time series retention like this:

```javascript
// https://redis.io/commands/ts.alter/
const alterResponse = await client.ts.alter('temperature', {
  RETENTION: 0 // Keep the entries forever
});
```

### Retrieving Information about the timeseries Stored in Redis

RedisTimeSeries also includes commands that can help to view the information on the state of a time series.

Using the [`TS.INFO`](https://redis.io/commands/ts.info/) command, we can view timeseries information like this:

```javascript
// Get some information about the state of the timeseries.
// https://redis.io/commands/ts.info/
const tsInfo = await client.ts.info('temperature');

// tsInfo looks like this:
// {
//   totalSamples: 1440,
//   memoryUsage: 28904,
//   firstTimestamp: 1641508920000,
//   lastTimestamp: 1641595320000,
//   retentionTime: 86400000,
//   chunkCount: 7,
//   chunkSize: 4096,
//   chunkType: 'uncompressed',
//   duplicatePolicy: 'block',
//   labels: [],
//   sourceKey: null,
//   rules: []
// }
```
