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docs(metrics): update Cypress example
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tdakkota committed Oct 3, 2023
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Expand Up @@ -35,7 +35,6 @@ in single time shard, so we don't add metric name to key.
2. Resource set
3. Attribute set


Data should be stored in a way that allows exploit low cardinality of metric name and resource set,
subsequently reducing series cardinality.

Expand Down Expand Up @@ -80,7 +79,7 @@ value: 10
We compute hashes from attribute sets:
| name | value |
|------------|----------------------------------|
| ---------- | -------------------------------- |
| attributes | 3b52e723db6a5e0aaee48e0a984d33f9 |
| resource | 4bfe5ab10b4f64a45383b67c222d962c |
Expand All @@ -89,41 +88,72 @@ Attribute set is represented by an ordered list attributes to make hash determin
#### `resource`

| key | value |
|----------------------------------|---------------------------------------------|
| -------------------------------- | ------------------------------------------- |
| 4bfe5ab10b4f64a45383b67c222d962c | `{"tenant.id": "1", "service.name": "api"}` |

#### `attributes`

| metric | key | value |
|-----------------------|----------------------------------|----------------------------|
| --------------------- | -------------------------------- | -------------------------- |
| http.request.duration | 3b52e723db6a5e0aaee48e0a984d33f9 | `{"foo": 1, "bar": "baz"}` |

#### `points`

| name | resource_hash | attribute_hash | timestamp | value |
|-----------------------|----------------------------------|----------------------------------|----------------------|-------|
| --------------------- | -------------------------------- | -------------------------------- | -------------------- | ----- |
| http.request.duration | 4bfe5ab10b4f64a45383b67c222d962c | 3b52e723db6a5e0aaee48e0a984d33f9 | 2021-01-01T00:00:00Z | 10 |

## Partitioning and sharding

Data should be sharded by time (e.g. per day, week or similar, can be heterogeneous) and tenant id.

For example:
Having:

1. closed data partitioned by day (Δ=1D)
2. active attributes and resources partitioned by hour (δ=1H)

```
/metrics
/tenant01
/2021-01-01 <- time shard
values
attributes
resources
/2021-01-02
/tenant02
/2021-01-01
/tenant-1
/active
points
attributes/
2021-01-01-T-20-00
2021-01-01-T-21-00
2021-01-01-T-22-00
2021-01-01-T-23-00
resources/
2021-01-01-T-20-00
2021-01-01-T-21-00
2021-01-01-T-22-00
2021-01-01-T-23-00
/closed
/2021-01-01
points
attributes
resources
```
- `/metrics/tenant/active/{attributes, resources}/*`:
dynamic tables of 2Δ data that is partitioned by δ (1H)
- `/metrics/tenant/points`:
dynamic table that stores 2Δ (2D) of point data
Each Δ:
1. Create new directory in `/metrics/tenant/closed`:
e.g. `/metrics/tenant1/closed/2021-01-01`
2. Copy `[T-2Δ, T-Δ)` data from `active/points` to `points` static table
3. Merge `[T-2Δ, T-Δ)` data from `active/attributes/\*` to `attributes` static table
4. Merge `[T-2Δ, T-Δ)` data from `active/resources/\*` to `resources` static table
5. Delete data that is older than `T-Δ` from active tables
Query can be efficiently executed concurrently.
Also, there are concurrent `LogQL` executors that can exploit that.
- https://github.com/thanos-io/promql-engine
## Query execution
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