---
title: Using pre-aggregations
description: Operational guide to aggregate awareness—how Cube picks rollups, partitions data, enforces rollup-only mode, and tunes pre-aggregation behavior.
---

Pre-aggregations is an implementation of aggregate awareness in Cube.
Pre-aggregation tables are materialized query results. 
Cube can analyze queries against a defined set of pre-aggregation rules to 
choose the optimal one that will be used to serve a given Cube query instead of going to the data source.

If Cube finds a suitable pre-aggregation rule, database querying becomes a
multi-stage process:

1.  Cube checks if an up-to-date copy of the pre-aggregation exists.

2.  Cube will execute a query against the pre-aggregated tables instead of the
    raw data.

Pre-aggregations is a powerful way to speed up your Cube queries. There are many
configuration options to consider. Please make sure to check the
[configuration reference][ref-schema-ref-preaggs].

## Matching queries

When executing a query, Cube will try to [match and fulfill it with a
pre-aggregation][ref-matching-preaggs] in the first place.

If there's no matching pre-aggregation, Cube will query the upstream data
source instead, unless the [rollup-only mode](#rollup-only-mode) is enabled.

## Rollup-only mode

In the rollup-only mode, Cube will **only** fulfill queries using
pre-aggregations. To enable the rollup-only mode, use the
[`CUBEJS_ROLLUP_ONLY`](/reference/configuration/environment-variables#cubejs_rollup_only) environment variable.

It can be useful to prevent queries from your end users from ever hitting the
upstream data source, e.g., if you prefer to use your data warehouse only to
build and refresh pre-aggregations and keep it suspended the rest of the time.

<Warning>

When the rollup-only mode is used with a single-node deployment (where the API
instance also serves as a [refresh worker][ref-deploy-refresh-wrkr]), queries
that can't be fulfilled with pre-aggregations will result in an error.
Scheduled refreshes will continue to work in the background.

</Warning>

## Refresh strategy

Refresh strategy can be customized by setting the
[`refresh_key`][ref-schema-ref-preaggs-refresh-key] property for the
pre-aggregation.

The default value of [`refresh_key`][ref-schema-ref-preaggs-refresh-key] is
`every: 1 hour`, if neither of the cubes overrides it's `refreshKey` parameter. 
It can be redefined either by overriding the default value of
the [`every` property][ref-schema-ref-preaggs-refresh-key-every]:

<CodeGroup>

```yaml title="YAML"
cubes:
  - name: orders
    # ...

    pre_aggregations:
      - name: amount_by_created
        type: rollup
        measures:
          - amount
        time_dimension: created_at
        granularity: month
        refresh_key:
          every: 12 hour
```

```javascript title="JavaScript"
cube(`orders`, {
  // ...

  pre_aggregations: {
    amount_by_created: {
      type: `rollup`,
      measures: [amount],
      time_dimension: created_at,
      granularity: `month`,
      refresh_key: {
        every: `12 hour`
      }
    }
  }
})
```

</CodeGroup>

Or by providing a [`sql` property][ref-schema-ref-preaggs-refresh-key-sql]
instead, and leaving `every` unchanged from its default value:

<CodeGroup>

```yaml title="YAML"
cubes:
  - name: orders
    # ...

    pre_aggregations:
      - name: amount_by_created
        measures:
          - amount
        time_dimension: created_at
        granularity: month
        refresh_key:
          # every will default to `10 seconds` here
          sql: SELECT MAX(created_at) FROM orders
```

```javascript title="JavaScript"
cube(`orders`, {
  // ...

  pre_aggregations: {
    amount_by_created: {
      measures: [amount],
      time_dimension: created_at,
      granularity: `month`,
      refresh_key: {
        // every will default to `10 seconds` here
        sql: `SELECT MAX(created_at) FROM orders`
      }
    }
  }
})
```

</CodeGroup>

Or both `every` and `sql` can be defined together:

<CodeGroup>

```yaml title="YAML"
cubes:
  - name: orders
    # ...

    pre_aggregations:
      - name: amount_by_created
        measures:
          - amount
        time_dimension: created_at
        granularity: month
        refresh_key:
          every: 12 hour
          sql: SELECT MAX(created_at) FROM orders
```

```javascript title="JavaScript"
cube(`orders`, {
  // ...

  pre_aggregations: {
    amount_by_created: {
      measures: [amount],
      time_dimension: created_at,
      granularity: `month`,
      refresh_key: {
        every: `12 hour`,
        sql: `SELECT MAX(created_at) FROM orders`
      }
    }
  }
})
```

</CodeGroup>

When `every` and `sql` are used together, Cube will run the query from the `sql`
property on an interval defined by the `every` property. If the query returns
new results, then the pre-aggregation will be refreshed.

## Partitioning

[Partitioning][wiki-partitioning] is an extremely effective optimization for
accelerating pre-aggregations build and refresh time. It effectively "shards"
the data between multiple tables, splitting them by a defined attribute.

Cube can be configured to incrementally refresh only the last set of partitions
through the `updateWindow` property. This leads to faster refresh times due to
unnecessary data not being reloaded, and even reduced cost for some databases
like [BigQuery](/admin/connect-to-data/data-sources/google-bigquery) or
[AWS Athena](/admin/connect-to-data/data-sources/aws-athena).

<Note>

See [this recipe][ref-incremental-refresh-recipe] for an example of optimized
incremental refresh.

</Note>

Any `rollup` pre-aggregation can be partitioned by time using the
`partition_granularity` property in [a pre-aggregation
definition][ref-schema-ref-preaggs]. In the example below, the
`partition_granularity` is set to `month`, which means Cube will generate
separate tables for each month's worth of data. Once built, it will continue to
refresh on a daily basis the last 3 months of data.

<CodeGroup>

```yaml title="YAML"
cubes:
  - name: orders
    # ...

    pre_aggregations:
      - name: category_and_date
        measures:
          - count
          - revenue
        dimensions:
          - category
        time_dimension: created_at
        granularity: day
        partition_granularity: month
        refresh_key:
          every: 1 day
          incremental: true
          update_window: 3 months
```

```javascript title="JavaScript"
cube(`orders`, {
  // ...

  preAggregations: {
    category_and_date: {
      measures: [count, revenue],
      dimensions: [category],
      time_dimension: created_at,
      granularity: `day`,
      partition_granularity: `month`,
      refresh_key: {
        every: `1 day`,
        incremental: true,
        update_window: `3 months`
      }
    }
  }
})
```

</CodeGroup>

### Partitioning by non-time dimension

Cube Store uses an auto-partitioning technique to split Cube logical partitions into multiple physical ones.
The partitioning key is the same as the sorting key of an index.
Every physical partition is stored as a separate parquet file.
Split is performed based on underlying parquet file sizes and rows inside those files.
So simplest way to ensure proper partitioning is to introduce an index.
For bigger pre-aggregations first columns of an index will determine the partitioning scheme.
An interesting consequence of having time dimension partitioning enabled with an index is data partitioned by time and then by sorting the key of an index.
It leads to that even in case of optimal index in place querying time is proportional to count of involved time partitions.
This issue can be addressed by lambda pre-aggregations.

Alternatively, if you want to explicitly introduce key partitioning, you can use multi-tenancy to introduce multiple orchestrator IDs.
Each orchestrator ID can use a different pre-aggregation schema, so you may define those based on the partitioning key you want to introduce.
This technique, together with multi-router Cube Store approach, allows you to achieve linear scaling on the partitioning key of your choice.

### Best practices

In general, it's better to lean towards less partitions, as long as you are satisfied with query speed.

For optimal _querying performance_, partitions should be small enough so that a Cube Store worker can read (scan) a partition in less than 100 milliseconds. 
The best way to optimize this is to start from a relatively large partition (e.g., yearly or no partition at all if data permits), 
check what the [flame graph][ref-flame-graph] in Query History shows, then iterate as needed.

For optimal pre-aggregation _build performance_, you would optimize partition size together with pre-aggregation build concurrency and build time. Smaller 
partitions with high concurrency would incur significant overhead. For optimal build performance, having 1 Cube Store worker per partition is ideal. 
However, Cube Store workers can handle up to 4 partitions per worker conucrrently. Since Cube Store workers often max out at 16, this means you should avoid having more than 64 partitions. 
Any additional partitions would be queued. Keep in mind that indexes essentially multiply the number of partitions that are created, 
so for example, if you have two indexes, you would want to avoid having more than 32 partitions to avoid queueing. 
The best way to optimize this is to make refresh keys as infrequent as possible 
and then use the [Build History][ref-build-history] tab to check build times, along with the [Performance Insights][ref-perf-insights] page to 
monitor Cube Store workers load, and iterate as needed.

## Using indexes

[Indexes][ref-ref-indexes] are sorted copies of pre-aggregation data.

**When you define a pre-aggregation without any explicit indexes, the default
index is created.** In this index, dimensions come first, time dimensions come
second.

When you define additional indexes, you don't incur any additional costs on
the data warehouse side. However, the pre-aggregation build time for a
particular pre-aggregation increases with each index because all indexes for pre-aggregation are built during ingestion time.

### When to use indexes?

At query time, if the default index can't be selected for a merge sort scan,
then a less performant hash aggregation would be used. It usually means that
the full table needs to be scanned to get query results.

It usually doesn't make much difference if the pre-aggregation table is only
several MBs in size. However, for larger pre-aggregations, indexes are usually
required to achieve optimal performance, especially if not all dimensions from
a pre-aggregation are used in a particular query.

### Best practices

Most pre-aggregations represent [additive][ref-additivity] rollups. For such
rollups, **the rule of thumb is that, for most queries, there should be
at least one index that makes a particular query scan very little amount of
data,** which makes it very fast. (There are exceptions to this rule like
top-k queries or queries with only low selectivity range filters. Optimization
for these use cases usually involves remodeling data and queries.)

To maximize performance, you can introduce an index per each query type so
that the set of dimensions used in a query overlaps as much as possible with
the set of dimensions in the index. Measures are usually only used in indexes
if you plan to filter on a measure value and the cardinality of the possible
values of the measure is low.

The order in which dimensions are specified in the index is **very** important;
suboptimal ordering can lead to diminished performance. To improve the
performance of an index the main thing to consider is its order of dimensions.
The rule of thumb for dimension order is as follows:

- Dimensions used in high selectivity, single-value filters come first.
- Dimensions used in `GROUP BY` come second.
- Everything else used in the query comes in the end, including dimensions
used in low selectivity, multiple-value filters.

It might sound counter-intuitive to have dimensions used in `GROUP BY` before
dimensions used in multiple-value filters. However, Cube Store always performs
scans on sorted data, and if `GROUP BY` matches index ordering, merge
sort-based algorithms are used for querying, which are usually much faster
than hash-based `GROUP BY` in case index ordering doesn't match the query.

If in doubt, always
[use `EXPLAIN` and `EXPLAIN ANALYZE`][ref-cubestore-sql-commands] to figure out
the final query plan.

#### Example

Suppose you have a pre-aggregation that has millions of rows and the following
structure:

| timestamp           | product_name  | product_category | zip_code | order_total |
| ------------------- | ------------- | ---------------- | -------- | ----------- |
| 2023-01-01 10:00:00 | Keyboard      | Electronics      | 88523    | 1000        |
| 2023-01-01 10:00:00 | Mouse         | Electronics      | 88523    | 800         |
| 2023-01-01 10:00:00 | Plastic Chair | Furniture        | 88523    | 2000        |
| 2023-01-01 11:00:00 | Keyboard      | Electronics      | 88524    | 2000        |
| 2023-01-01 11:00:00 | Plastic Chair | Furniture        | 88524    | 3000        |

The pre-aggregation definition looks as follows:

<CodeGroup>

```yaml title="YAML"
cubes:
  - name: orders
    # ...

    pre_aggregations:
      - name: main
        measures:
          - order_total
        dimensions:
          - product_name
          - product_category
          - zip_code
        time_dimension: timestamp
        granularity: hour
        partition_granularity: day
        allow_non_strict_date_range_match: true
        refresh_key:
          every: 1 hour
          incremental: true
          update_window: 1 day
        build_range_start:
          sql: SELECT DATE_SUB(NOW(), 365)
        build_range_end:
          sql: SELECT NOW()
```

```javascript title="JavaScript"
cube("orders", {
  // ...

  pre_aggregations: {
    main: {
      measures: [order_total],
      dimensions: [product_name, product_category, zip_code],
      time_dimension: timestamp,
      granularity: `hour`,
      partition_granularity: `day`,
      allow_non_strict_date_range_match: true,
      refresh_key: {
        every: `1 hour`,
        incremental: true,
        update_window: `1 day`
      },
      build_range_start: {
        sql: `SELECT DATE_SUB(NOW(), 365)`
      },
      build_range_end: {
        sql: `SELECT NOW()`
      }
    }
  }
})
```

</CodeGroup>

You run the following query on a regular basis, with the only difference
between queries being the filter values:

```json
{
  "measures": [
    "orders.order_total"
  ],
  "timeDimensions": [
    {
      "dimension": "orders.timestamp",
      "granularity": "hour",
      "dateRange": [
        "2022-12-14T06:00:00.000",
        "2023-01-13T06:00:00.000"
      ]
    }
  ],
  "order": {
    "orders.timestamp": "asc"
  },
  "filters": [
    {
      "member": "orders.product_category",
      "operator": "equals",
      "values": [
        "Electronics"
      ]
    },
    {
      "member": "orders.product_name",
      "operator": "equals",
      "values": [
        "Keyboard",
        "Mouse"
      ]
    }
  ],
  "dimensions": [
    "orders.zip_code"
  ],
  "limit": 10000
}
```

After running this query on a dataset with millions of records you find that
it's taking too long to run, so you decide to add an index to target this
specific query. Taking into account the best practices, you should define an
index as follows:

<CodeGroup>

```yaml title="YAML"
cubes:
  - name: orders
    # ...

    pre_aggregations:
      - name: main
        # ...

        indexes:
          - name: category_productname_zipcode_index
            columns:
              - product_category
              - zip_code
              - product_name
```

```javascript title="JavaScript"
cube(`orders`, {
  // ...

  pre_aggregations: {
    main: {
      // ...

      indexes: {
        category_productname_zipcode_index: {
          columns: [
            product_category,
            zip_code,
            product_name
          ]
        }
      }
    }
  }
})
```

</CodeGroup>

Here's why:

- The `product_category` dimension comes first as it's used in a single-value
filter.
- Then, the `zip_code` dimension comes second as it's used in `GROUP BY`.
- The `product_name` dimension comes last as it's used in a multiple-value
filter.

The data within `category_productname_zipcode_index` would look as follows:

| product_category | zip_code | product_name  | timestamp           | order_total |
| ---------------- | -------- | ------------- | ------------------- | ----------- |
| Electronics      | 88523    | Mouse         | 2023-01-01 10:00:00 | 800         |
| Electronics      | 88523    | Plastic Chair | 2023-01-01 10:00:00 | 2000        |
| Furniture        | 88523    | Keyboard      | 2023-01-01 10:00:00 | 1000        |
| Electronics      | 88524    | Keyboard      | 2023-01-01 11:00:00 | 2000        |
| Furniture        | 88524    | Plastic Chair | 2023-01-01 11:00:00 | 3000        |

### Aggregating indexes

A regular index (the kind described above) is a _sorted copy_ of the full
pre-aggregation: it contains the same rows at the same granularity, just ordered
differently so that a particular query can be served with a fast merge scan. An
**aggregating index** goes one step further. It stores **only** the dimensions
listed in its definition, together with the pre-aggregated measures, and rolls the
data up over every dimension that is _not_ in the index.

**In other words, an aggregating index is a rollup of the data that already lives
inside a rollup table.** Cube Store aggregates over the missing dimensions once,
when the index is built. At query time that work is already done, so a query that
matches the index reads far fewer rows and skips the aggregation step entirely.

Take the `main` pre-aggregation [from above](#example). A regular index keeps
every `timestamp` / `product_name` / `product_category` / `zip_code` combination.
An aggregating index on `zip_code` alone collapses all of that into one row per
ZIP code:

| zip_code | order_total |
| -------- | ----------- |
| 88523    | 3800        |
| 88524    | 5000        |

#### When to use an aggregating index

Reach for an aggregating index when you have a **wide** pre-aggregation (many
dimensions) that is frequently queried on a **small, fixed subset** of those
dimensions. The classic example is a rollup with 50 dimensions where a recurring
dashboard tile only ever groups by 5 of them. The aggregating index materializes
exactly that narrow shape, so the tile reads a tiny pre-rolled table instead of
scanning and re-aggregating the full pre-aggregation.

The decision is usually between an aggregating index and a _second
pre-aggregation_ for the same narrow shape. The economics favor the index:

- Each pre-aggregation is built by querying your data source independently, so
_N_ pre-aggregations means _N_ expensive warehouse reads.
- A single pre-aggregation with _N_ aggregating indexes is built from **one**
warehouse read — Cube Store derives every index locally during ingestion.

You get roughly the same Cube Store query performance at a fraction of the
warehouse cost. The cost is paid in pre-aggregation _build_ time, which grows with
each index.

The following table summarizes how the two index types differ:

| | Regular index | Aggregating index |
| --- | --- | --- |
| `type` | `regular` (default; can be omitted) | `aggregate` |
| What it stores | All pre-aggregation rows, re-sorted | Only the index dimensions + rolled-up measures |
| Granularity | Same as the pre-aggregation | Rolled up to the index dimensions |
| Size | Same row count as the pre-aggregation | Typically much smaller |
| Supported measures | Any | [Additive][ref-additivity] only |
| Query dimensions | Any (affects only sort efficiency) | **All** must be columns of the index |
| Filters | Any dimension | Only on dimensions that are columns of the index |
| Best for | Tuning sort order for known shapes; flexible and ad-hoc queries | A wide pre-aggregation queried on a narrow, fixed subset |

#### How regular and aggregating indexes work together

You don't have to choose one _or_ the other. You can define both kinds on the same
pre-aggregation, and Cube Store selects the best index for each query
automatically:

- When a query **qualifies** for an aggregating index, Cube Store **prefers it**
over regular indexes, because it is smaller and already rolled up. When several
aggregating indexes qualify, the one with the smallest key wins.
- Any query that does **not** qualify falls back to a regular index (or the
default index), so those queries are still optimized.

This is why the recommended pattern for a wide pre-aggregation is a couple of
regular indexes covering your general and ad-hoc query shapes, _plus_ one
aggregating index per hot narrow query shape.

A query qualifies for an aggregating index only when **all** of the following
hold:

- Every measure used in the query is [additive][ref-additivity] — for example,
measures built on `sum`, `count`, `min`, `max`, or `countDistinctApprox`.
Non-additive measures (such as `avg` or exact `countDistinct`) can never use an
aggregating index, because they cannot be re-derived from a partially rolled-up
result.
- **Every** dimension used in the query is one of the index's columns.
- **Every** filter is on a dimension that is one of the index's columns. You
cannot filter on a dimension that was rolled away when the index was built.

#### Defining aggregating indexes

Aggregating indexes are defined by adding the [`type` option][ref-ref-index-type]
to an index definition. Define them alongside your regular indexes so that Cube
Store can route each query to the most efficient one:

<CodeGroup>

```yaml title="YAML"
cubes:
  - name: orders
    # ...

    pre_aggregations:
      - name: main
        # ...

        indexes:
          # ...

          - name: zip_code_index
            columns:
              - zip_code
            type: aggregate
```

```javascript title="JavaScript"
cube("orders", {
  // ...

  pre_aggregations: {
    main: {
      // ...

      indexes: {
        // ...

        zip_code_index: {
          columns: [zip_code],
          type: `aggregate`
        }
      }
    }
  }
})
```

</CodeGroup>

The data for `zip_code_index` would look as follows:

| zip_code | order_total |
| -------- | ----------- |
| 88523    | 3800        |
| 88524    | 5000        |

#### Putting it together

The two index types are most useful in combination. Building on the `main`
pre-aggregation from above, define both the regular index that targets your
filtered, multi-dimension query and the aggregating index that targets the narrow
ZIP-code rollup:

<CodeGroup>

```yaml title="YAML"
cubes:
  - name: orders
    # ...

    pre_aggregations:
      - name: main
        measures:
          - order_total
        dimensions:
          - product_name
          - product_category
          - zip_code
        time_dimension: timestamp
        granularity: hour
        # ...

        indexes:
          # Regular index: full-grain, re-sorted for the filtered query
          - name: category_productname_zipcode_index
            columns:
              - product_category
              - zip_code
              - product_name

          # Aggregating index: pre-rolled to a single dimension
          - name: zip_code_index
            columns:
              - zip_code
            type: aggregate
```

```javascript title="JavaScript"
cube("orders", {
  // ...

  pre_aggregations: {
    main: {
      measures: [order_total],
      dimensions: [product_name, product_category, zip_code],
      time_dimension: timestamp,
      granularity: `hour`,
      // ...

      indexes: {
        // Regular index: full-grain, re-sorted for the filtered query
        category_productname_zipcode_index: {
          columns: [product_category, zip_code, product_name]
        },

        // Aggregating index: pre-rolled to a single dimension
        zip_code_index: {
          columns: [zip_code],
          type: `aggregate`
        }
      }
    }
  }
})
```

</CodeGroup>

Now consider two queries against this pre-aggregation.

**Query A — totals per ZIP code.** It groups by a single dimension that is a
column of the aggregating index, uses only an additive measure, and applies no
disqualifying filters:

```json
{
  "measures": ["orders.order_total"],
  "dimensions": ["orders.zip_code"]
}
```

This query **qualifies for `zip_code_index`**, so Cube Store serves it from the
two-row aggregating index rather than scanning the full pre-aggregation — even
though the regular index also contains `zip_code`, the smaller aggregating index
is preferred.

**Query B — filtered breakdown by product.** It filters on `product_category` and
groups by `product_name`:

```json
{
  "measures": ["orders.order_total"],
  "dimensions": ["orders.product_name"],
  "filters": [
    {
      "member": "orders.product_category",
      "operator": "equals",
      "values": ["Electronics"]
    }
  ]
}
```

This query **cannot** use `zip_code_index`: it groups by and filters on dimensions
that were rolled away. Cube Store automatically **falls back to the regular
`category_productname_zipcode_index`**, whose column order (`product_category`
first for the single-value filter, then the rest) lets it serve the query with a
fast merge scan.

With both indexes defined, each query is routed to the most efficient one without
any change to the query itself. Use [`EXPLAIN`][ref-cubestore-sql-commands] to
confirm which index a given query selects.

### Compaction

Whenever a newer version of pre-aggregation is just built and becomes available its performance would be suboptimal as it's pending compaction.
Most of the essential compaction process usually takes several seconds to several minutes for bigger partitions after pre-aggregation creation, depending on the size of the partition and the Cube Store workers' processing power available.
This compaction process is usually unnoticeable for queries that are optimal in terms of index usage, so it's always best practice to make sure all of your queries match an index.

## Inspecting pre-aggregations

Cube Store partially supports the MySQL protocol. This allows you to execute
simple queries using a familiar SQL syntax. You can connect using the MySQL CLI
client, for example:

```bash
mysql -h <CUBESTORE_IP> --user=cubestore -pcubestore --protocol=TCP
```

<Warning>

Only Linux and Mac OS versions of MySQL client are supported as of right now.
You can install one on ubuntu using `apt-get install default-mysql-client`
command or `brew install mysql-client` on Mac OS. Windows versions of the MySQL
client aren't supported.

</Warning>

To check which pre-aggregations are managed by Cube Store, you could run the
following query:

```sql
SELECT * FROM information_schema.tables;
+----------------------+-----------------------------------------------+
| table_schema         | table_name                                    |
+----------------------+-----------------------------------------------+
| dev_pre_aggregations | orders_main20190101_23jnqarg_uiyfxd0f_1gifflf |
| dev_pre_aggregations | orders_main20190301_24ph0a1c_utzntnv_1gifflf  |
| dev_pre_aggregations | orders_main20190201_zhrh5kj1_rkmsrffi_1gifflf |
| dev_pre_aggregations | orders_main20191001_mdw2hxku_waxajvwc_1gifflf |
| dev_pre_aggregations | orders_main20190701_izc2tl0h_bxsf1zlb_1gifflf |
+----------------------+-----------------------------------------------+
5 rows in set (0.01 sec)
```

These pre-aggregations are stored as Parquet files under the `.cubestore/`
folder in the project root during development.

### `EXPLAIN` queries {#explain-queries}

Cube Store's MySQL protocol also supports `EXPLAIN`, `EXPLAIN ANALYZE`, and
`EXPLAIN ANALYZE DETAILED`, all of which are useful for determining how much
processing a query will require, and for seeing where time is spent within it.

See [Cube Store SQL commands][ref-cubestore-sql-commands] for their syntax and
output, guidance on reading a query plan, and the rest of Cube Store's command
surface.

## Pre-aggregations storage

Cube uses its own purpose-built pre-aggregations engine: [Cube Store][ref-cube-store].

When using Cube Store, pre-aggregation data will be ingested and stored as Parquet files
on a [blob storage][ref-cube-store-storage]. Then, Cube Store would load that data to
execute queries using pre-aggregations.

However, [`original_sql`][ref-original-sql] pre-aggregations are stored in the data source
by default. It is not recommended to store `original_sql` pre-aggregations in Cube Store.

## Joins between pre-aggregations

When making a query that joins data from two different cubes, Cube can use
pre-aggregations instead of running the base SQL queries. To get started, first
ensure both cubes have valid pre-aggregations:

<CodeGroup>

```yaml title="YAML"
cubes:
  - name: orders
    # ...

    pre_aggregations:
      - name: orders_rollup
        measures:
          - CUBE.count
        dimensions:
          - CUBE.user_id
          - CUBE.status
        time_dimension: CUBE.created_at
        granularity: day

    joins:
      - name: users
        sql: "{CUBE.user_id} = ${users.id}"
        relationship: many_to_one

  - name: users
    # ...

    pre_aggregations:
      - name: users_rollup
        dimensions:
          - CUBE.id
          - CUBE.name
```

```javascript title="JavaScript"
cube(`orders`, {
  // ...

  pre_aggregations: {
    orders_rollup: {
      measures: [CUBE.count],
      dimensions: [CUBE.user_id, CUBE.status],
      time_dimension: CUBE.created_at,
      granularity: `day`
    }
  },

  joins: {
    users: {
      sql: `${CUBE.user_id} = ${users.id}`,
      relationship: `many_to_one`
    }
  }
})

cube(`users`, {
  // ...

  pre_aggregations: {
    users_rollup: {
      dimensions: [CUBE.id, CUBE.name]
    }
  }
})
```

</CodeGroup>

Before we continue, let's add an index to the `orders_rollup` pre-aggregation so
that the `rollup_join` pre-aggregation can work correctly:

<CodeGroup>

```yaml title="YAML"
cubes:
  - name: orders
    # ...

    pre_aggregations:
      - name: orders_rollup
        # ...

        indexes:
          - name: user_index
            columns:
              - CUBE.user_id
```

```javascript title="JavaScript"
cube(`orders`, {
  // ...

  pre_aggregations: {
    orders_rollup: {
      // ...

      indexes: {
        user_index: {
          columns: [CUBE.user_id]
        }
      }
    }
  }
})
```

</CodeGroup>

Now we can add a new pre-aggregation of type `rollup_join` to the `orders` cube:

<CodeGroup>

```yaml title="YAML"
cubes:
  - name: orders
    # ...

    pre_aggregations:
      # ...

      - name: orders_with_users_rollup
        type: rollup_join
        measures:
          - CUBE.count
        dimensions:
          - users.name
        time_dimension: CUBE.created_at
        granularity: day
        rollups:
          - users.users_rollup
          - CUBE.orders_rollup
```

```javascript title="JavaScript"
cube(`orders`, {
  // ...

  pre_aggregations: {
    // ...

    orders_with_users_rollup: {
      type: `rollup_join`,
      measures: [CUBE.count],
      dimensions: [users.name],
      time_dimension: CUBE.created_at,
      granularity: `day`,
      rollups: [users.users_rollup, CUBE.orders_rollup]
    }
  }
})
```

</CodeGroup>

With all of the above set up, making a query such as the following will now use
`orders.orders_rollup` and `users.users_rollup`, avoiding a database request:

```json
{
  "dimensions": ["users.name"],
  "timeDimensions": [
    {
      "dimension": "orders.created_at",
      "dateRange": "This month"
    }
  ],
  "order": {
    "orders.count": "desc"
  },
  "measures": ["orders.count"]
}
```

## Pre-Aggregation build strategies

<Info>

For ideal performance, pre-aggregations should be built using a dedicated
Refresh Worker. [See here for more details][ref-prod-list-refresh].

</Info>

Cube supports three different strategies for building pre-aggregations. To see
which strategies your database supports, please refer to its individual page
from [Connecting to the Database][ref-config-db].

### Simple

When using the simple strategy, Cube will use the source database as a temporary
staging area for writing pre-aggregations to determine column types. The data is
loaded back into memory before writing them to Cube Store (or an external
database).

<Info>

For larger datasets, we strongly recommend using the [Batching][self-batching]
or [Export Bucket][self-export-bucket] strategies instead.

</Info>

<div style={{ textAlign: "center" }}>
  <img
    alt="Internal vs External vs External with Cube Store diagram"
    src="https://ucarecdn.com/9aee19aa-2d52-4022-928d-5c97be9417e5/"
    style={{ border: "none" }}
    width="100%"
  />
</div>

### Batching

Batching is a more performant strategy where Cube sends compressed CSVs for Cube
Store to ingest.

<div style={{ textAlign: "center" }}>
  <img
    alt="Internal vs External vs External with Cube Store diagram"
    src="https://ucarecdn.com/deb0194a-f7cb-49bc-84a6-d07bdeb8bd36/"
    style={{ border: "none" }}
    width="100%"
  />
</div>

The performance scales to the amount of memory available on the Cube instance.
Batching is automatically enabled for any databases that can support it.

### Export bucket

<Warning>

The export bucket strategy requires permission to execute `CREATE TABLE`
statements in the data source as part of the pre-aggregation build process.

</Warning>

<Warning>

Do not confuse the export bucket with the Cube Store storage bucket. Those are
two separate storages and should never be mixed.

</Warning>

When dealing with larger pre-aggregations (more than 100k rows), performance can
be significantly improved by using an export bucket. This allows the source
database to temporarily materialize the data locally, which is then loaded into
Cube Store in parallel:

<div style={{ textAlign: "center" }}>
  <img
    alt="Internal vs External vs External with Cube Store diagram"
    src="https://ucarecdn.com/f43999c4-cf91-4d36-9650-3078312fb9c9/"
    style={{ border: "none" }}
    width="100%"
  />
</div>

Enabling the export bucket functionality requires extra configuration and is 
not available for all data sources. Please
refer to the [database-specific documentation][ref-config-db] for more details. 
Data sources that support export buckets will have an "Export Bucket" section with more information.

When using cloud storage, it is important to correctly configure any data
retention policies to clean up the data in the export bucket as Cube does not
currently manage this. For most use-cases, 1 day is sufficient.

## Streaming pre-aggregations

Streaming pre-aggregations are different from traditional pre-aggregations in
the way they are being updated. Traditional pre-aggregations follow the “pull”
model — Cube **pulls updates** from the data source based on some cadence and/or
condition. Streaming pre-aggregations follow the “push” model — Cube
**subscribes to the updates** from the data source and always keeps
pre-aggregation up to date.

You don’t need to define `refresh_key` for streaming pre-aggregations. Whether
pre-aggregation is streaming or not is defined by the data source.

Currently, Cube supports only one streaming data source -
[ksqlDB](/admin/connect-to-data/data-sources/ksqldb). All pre-aggregations where
data source is ksqlDB are streaming.

We are working on supporting more data sources for streaming pre-aggregations,
please [let us know](https://cube.dev/contact) if you are interested in early
access to any of these drivers or would like Cube to support any other SQL
streaming engine.

## Troubleshooting

### Unused pre-aggregations

You might find that a pre-aggregation is ignored by Cube. Possible reasons:

* A pre-aggregation does not reference any dimensions or measures from a cube
where this pre-aggreation is [defined][ref-schema-ref-preaggs]. To resolve,
move it to another cube.
* A pre-aggregation is defined similarly to another pre-aggregation that has
more granular [partitions](#partitioning). To resolve, remove one of these
pre-aggregations.

### Members with unknown types

When building pre-aggregations, you might get an error similar to the this one:

```text
Error during create table: CREATE TABLE <REDACTED>:
Custom type 'fixed' is not supported
```

It means that a member of a pre-aggregation has a type in the upstream data
source that Cube Store can not recognize (e.g., `fixed` in this case).

To resolve, please add a cast to a known type in the [`sql` parameter][ref-member-sql]
of this member. For numeric types, it will most likely be an integer, a float,
or a decimal type, depending on the nature of your data.


[ref-config-db]: /admin/connect-to-data/data-sources
[ref-schema-ref-preaggs]: /reference/data-modeling/pre-aggregations
[ref-schema-ref-preaggs-refresh-key]: /reference/data-modeling/pre-aggregations#refresh_key
[ref-schema-ref-preaggs-refresh-key-every]: /reference/data-modeling/pre-aggregations#every
[ref-schema-ref-preaggs-refresh-key-sql]: /reference/data-modeling/pre-aggregations#sql
[ref-deploy-refresh-wrkr]: /cube-core/architecture#refresh-worker
[ref-prod-list-refresh]: /cube-core/running-in-production#set-up-refresh-worker
[ref-original-sql]: /reference/data-modeling/pre-aggregations#original_sql
[self-batching]: #batching
[self-export-bucket]: #export-bucket
[wiki-partitioning]: https://en.wikipedia.org/wiki/Partition_(database)
[ref-ref-indexes]: /reference/data-modeling/pre-aggregations#indexes
[ref-additivity]: /docs/pre-aggregations/matching-pre-aggregations
[ref-ref-index-type]: /reference/data-modeling/pre-aggregations#type-1
[ref-flame-graph]: /admin/monitoring/query-history#flame-graph
[ref-perf-insights]: /admin/monitoring/performance
[ref-build-history]: /admin/monitoring/pre-aggregations#build-history
[ref-matching-preaggs]: /docs/pre-aggregations/matching-pre-aggregations
[ref-cube-store]: /docs/pre-aggregations/running-in-production#architecture
[ref-cubestore-sql-commands]: /reference/cubestore/sql-commands
[ref-cube-store-storage]: /docs/pre-aggregations/running-in-production#storage
[ref-member-sql]: /reference/data-modeling/dimensions#sql
[ref-incremental-refresh-recipe]: /recipes/pre-aggregations/incrementally-building-pre-aggregations-for-a-date-range