Using pre-aggregations

Pre-aggregations is a powerful way to speed up your Cube queries. There are many configuration options to consider. Please make sure to also check the Pre-Aggregations reference in the data modeling section.

Refresh Strategy

Refresh strategy can be customized by setting the refresh_key property for the pre-aggregation.

The default value of 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:

YAML

JavaScript

cubes:
  - name: orders
    # ...
 
    pre_aggregations:
      - name: amount_by_created
        type: rollup
        measures:
          - amount
        time_dimension: created_at
        granularity: month
        refresh_key:
          every: 12 hour

Or by providing a sql property instead, and leaving every unchanged from its default value:

YAML

JavaScript

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

Or both every and sql can be defined together:

YAML

JavaScript

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

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.

Rollup Only Mode

To make Cube only serve requests from pre-aggregations, the CUBEJS_ROLLUP_ONLY environment variable can be set to true on an API instance. This will prevent serving data on API requests from the source database.

When using this configuration in a single node deployment (where the API instance and Refresh Worker are configured on the same host), requests made to the API that cannot be satisfied by a rollup throw an error. Scheduled refreshes will continue to work in the background.

Partitioning

Partitioning (opens in a new tab) 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 or AWS Athena.

Any rollup pre-aggregation can be partitioned by time using the partition_granularity property in a pre-aggregation definition. 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.

YAML

JavaScript

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

Using Indexes

When to use indexes?

Indexes are great when you filter large amounts of data across one or several dimension columns. You can read more about them here.

Best Practices

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

The order in which columns 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 the order of the columns defined in it.

The rule of thumb for index column order is:

Single value filters come first
GROUP BY columns come second
Everything else used in the query comes afterward

Example:

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

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

The pre-aggregation code would look as follows:

YAML

JavaScript

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()

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

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

YAML

JavaScript

cubes:
  - name: orders
    # ...
 
    pre_aggregations:
      - name: main
        # ...
 
        indexes:
          - name: category_productname_zipcode_index
            columns:
              - product_category
              - zip_code
              - product_name

Then the data within category_productname_zipcode_index would look like:

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

product_category column comes first as it's a single value filter. Then zip_code as it's GROUP BY column. product_name comes last as it's a multiple value filter.

It might sound counter-intuitive to have GROUP BY columns before filter ones, 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 of index ordering doesn't match the query. If in doubt, always use EXPLAIN and EXPLAIN ANALYZE in Cube Store to figure out the final query plan.

Aggregated indexes

Aggregated indexes can be defined as well. You can read more about them here.

Example:

YAML

JavaScript

cubes:
  - name: orders
    # ...
 
    pre_aggregations:
      - name: main
        # ...
 
        indexes:
          # ...
 
          - name: zip_code_index
            columns:
              - zip_code
            type: aggregate

And the data for zip_code_index would look like the following:

zip_code	order_total
88523	3800
88524	5000

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:

mysql -h <CUBESTORE_IP> --user=cubestore -pcubestore

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.

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

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

Cube Store's MySQL protocol also supports EXPLAIN and EXPLAIN ANALYZE queries both of which are useful for determining how much processing a query will require.

EXPLAIN queries show the logical plan for a query:

 EXPLAIN SELECT orders__platform, orders__gender, sum(orders__count) FROM dev_pre_aggregations.orders_general_o32v4dvq_vbyemtl2_1h5hs8r
 GROUP BY orders__gender, orders__platform;
+-------------------------------------------------------------------------------------------------------------------------------------+
| logical plan                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+--------------------------------------------------------------------------------------------------------------------------------------+
| Projection, [dev_pre_aggregations.orders_general_o32v4dvq_vbyemtl2_1h5hs8r.orders__platform, dev_pre_aggregations.orders_general_o32v4dvq_vbyemtl2_1h5hs8r.orders__gender, SUM(dev_pre_aggregations.orders_general_o32v4dvq_vbyemtl2_1h5hs8r.orders__count)]
  Aggregate
    ClusterSend, indices: [[96]]
      Scan dev_pre_aggregations.orders_general_o32v4dvq_vbyemtl2_1h5hs8r, source: CubeTable(index: orders_general_plat_gender_o32v4dvq_vbyemtl2_1h5hs8r:96:[123, 126]), fields: [orders__gender, orders__platform, orders__count] |
+-------------------------------------------------------------------------------------------------------------------------------------+

EXPLAIN ANALYZE queries show the physical plan for the router and all workers used for query processing:

 EXPLAIN ANALYZE SELECT orders__platform, orders__gender, sum(orders__count) FROM dev_pre_aggregations.orders_general_o32v4dvq_vbyemtl2_1h5hs8r
 GROUP BY orders__gender, orders__platform
 
+-----------+-----------------+--------------------------------------------------------------------------------------------------------------------------+
| node type | node name       | physical plan                                                                                                                                                                                                                                                                                                                                                   |
+-----------+-----------------+--------------------------------------------------------------------------------------------------------------------------+
| router    |                 | Projection, [orders__platform, orders__gender, SUM(dev_pre_aggregations.orders_general_o32v4dvq_vbyemtl2_1h5hs8r.orders__count)@2:SUM(orders__count)]
  FinalInplaceAggregate
    ClusterSend, partitions: [[123, 126]]                                                                                                                                         |
| worker    | 127.0.0.1:10001 | PartialInplaceAggregate
  Merge
    Scan, index: orders_general_plat_gender_o32v4dvq_vbyemtl2_1h5hs8r:96:[123, 126], fields: [orders__gender, orders__platform, orders__count]
      Projection, [orders__gender, orders__platform, orders__count]
        ParquetScan, files: /.cubestore/data/126-0qtyakym.parquet |
+-----------+-----------------+--------------------------------------------------------------------------------------------------------------------------+

Pre-Aggregations Storage

The default pre-aggregations storage in Cube is its own purpose-built storage layer: Cube Store.

Alternatively, you can store pre-aggregations internally in the source database. To store a pre-aggregation internally, set external: false in pre-aggregation definition.

Please note, that original_sql pre-aggregations are stored internally 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:

YAML

JavaScript

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

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

YAML

JavaScript

cubes:
  - name: orders
    # ...
 
    pre_aggregations:
      - name: orders_rollup
        # ...
 
        indexes:
          - name: user_index
            columns:
              - CUBE.user_id

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

YAML

JavaScript

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

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:

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

Pre-Aggregation Build Strategies

For ideal performance, pre-aggregations should be built using a dedicated Refresh Worker. See here for more details.

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.

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).

For larger datasets, we strongly recommend using the Batching or Export Bucket strategies instead.

Internal vs External vs External with Cube Store diagram

Batching

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

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

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

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

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:

Enabling the export bucket functionality requires extra configuration; please refer to the database-specific documentation for more details:

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. 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 (opens in a new tab) if you are interested in early access to any of these drivers or would like Cube to support any other SQL streaming engine.

Getting started with pre-aggregations Lambda pre-aggregations