# Performance

Metarank is a secondary re-ranker: it's an extra **non-free** step in your retrieval process. In a typical scenario, you should expect the following extras:

* Re-ranking latency: 10-30 ms
* Redis memory usage: 1-10 GiB
* Data import throughput: 1000-3000 events/second.

## Response latency

On a [RankLens](https://github.com/metarank/ranklens) dataset in a [synthetic latency test](https://github.com/metarank/metarank/blob/master/src/test/scala/ai/metarank/util/performance/LatencyBenchmark.scala), we observed the following distribution:

![Response latency depends on request size](/files/pOoQ0uHdjhwNWBp6oKF6)

Each Metarank installation is unique, but there are common things affecting the overall latency:

* [**State encoding format**](/reference/overview/persistence.md#state-encoding-formats): `binary` is faster than `json` due to its compact representation.
* **Metarank-Redis network latency**: Metarank pulls all features for all re-ranked items in a single large batch. There are no multiple network calls and only a constant overhead.
* **Request size**: the more items you ask to re-rank, the more data needs to be loaded.
* [**A number of feature extractors**](/reference/overview/feature-extractors.md): the more per-item features are defined in the config, the more data is loaded during the request processing.

So while planning your installation, expect Metarank to be within **20-30 ms** latency budget.

## Memory usage

Using the same reference [RankLens](https://github.com/metarank/ranklens) dataset, we built a fuzzy [synthetic dataset generator](https://github.com/metarank/metarank/blob/master/src/test/scala/ai/metarank/util/SyntheticRanklensDataset.scala) and generated the following dataset variations:

* N users, 100k items.
* Each user made 2 rankings within a single session.
* Each ranking event has 2 clicks made by the user.

| Users | Items | Rankings | Clicks | Total events | Uncompressed size |
| ----- | ----- | -------- | ------ | ------------ | ----------------- |
| 128K  | 100K  | 256K     | 512K   | 896K         | 287MiB            |
| 256K  | 100K  | 512K     | 1M     | 1.8M         | 512MiB            |
| 512K  | 100K  | 1M       | 2M     | 3.5M         | 963MiB            |
| 1M    | 100K  | 2M       | 4M     | 7.1M         | 1.8GiB            |
| 2M    | 100K  | 4M       | 8M     | 14.3M        | 3.5GiB            |
| 4M    | 100K  | 8M       | 16M    | 28.6M        | 7.1GiB            |

Metarank only tracks aggregated data required for the re-ranking and does not store raw events. Therefore, memory usage depends on the following characteristics of your dataset:

* **A number of unique users**: per-user click-through events are used as input for the ML model training.
* **A number of items**: if you define per-item feature extractors (like [`string`](/reference/overview/feature-extractors/scalar.md#string-extractors) or [`number`](/reference/overview/feature-extractors/scalar.md#numerical-extractor)), current point-in-time values of used fields are persisted.
* **A number of users**: if you define per-user features like [`interacted_with`](/reference/overview/feature-extractors/user-session.md#interacted-with), we persist a per-user list of interacted items.
* **A number of features**: stored click-through events also contain snapshots of all per-item feature values used to build the ranking back in time.

The resulting memory usage for a `binary` state encoding format and `Redis` as a persistence store is shown in the diagram below:

![Redis/heap memory usage](/files/sAUuCuLnvitlczYISv6K)

| Users, thousands | Redis, MB | Import time, min |
| ---------------- | --------- | ---------------- |
| 128              | 386       | 8                |
| 256              | 448       | 16               |
| 512              | 561       | 30               |
| 1024             | 812       | 61               |
| 2048             | 1210      | 110              |
| 4096             | 1860      | 205              |

So while planning your installation, expect Metarank to use around **0.8 GiB per 1M users**.


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