> ## Documentation Index > Fetch the complete documentation index at: https://cubed3-pavel-claude-modest-planck-ceg4c0.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Lambda pre-aggregations > Lambda-style pre-aggregations that merge historical rollups with fresher source or streaming layers for near-real-time serving on Cube Store. Lambda pre-aggregations follow the [Lambda architecture](https://en.wikipedia.org/wiki/Lambda_architecture) design to union real-time and batch data. Cube acts as a serving layer and uses pre-aggregations as a batch layer and source data or other pre-aggregations, usually [streaming][streaming-pre-agg], as a speed layer. Due to this design, lambda pre-aggregations **only** work with data that is newer than the existing batched pre-aggregations. Lambda pre-aggregations only work with Cube Store. ## Use cases Below we are looking at the most common examples of using lambda pre-aggregations. ### Batch and source data Batch data is coming from pre-aggregation and real-time data is coming from the data source.
Lambda pre-aggregation batch and source diagram
First, you need to create pre-aggregations that will contain your batch data. In the following example, we call it `batch`. Please note, it must have a `time_dimension` and `partition_granularity` specified. Cube will use these properties to union batch data with freshly-retrieved source data. You may also control the batch part of your data with the `build_range_start` and `build_range_end` properties of a pre-aggregation to determine a specific window for your batched data. Next, you need to create a lambda pre-aggregation. To do that, create pre-aggregation with type `rollup_lambda`, specify rollups you would like to use with `rollups` property, and finally set `union_with_source_data: true` to use source data as a real-time layer. Please make sure that the lambda pre-aggregation definition comes first when defining your pre-aggregations. ```yaml title="YAML" theme={null} cubes: - name: users # ... pre_aggregations: - name: lambda type: rollup_lambda union_with_source_data: true rollups: - CUBE.batch - name: batch measures: - users.count dimensions: - users.name time_dimension: users.created_at granularity: day partition_granularity: day build_range_start: sql: SELECT '2020-01-01' build_range_end: sql: SELECT '2022-05-30' ``` ```javascript title="JavaScript" theme={null} cube("users", { // ... pre_aggregations: { lambda: { type: `rollup_lambda`, union_with_source_data: true, rollups: [CUBE.batch] }, batch: { measures: [users.count], dimensions: [users.name], time_dimension: users.created_at, granularity: `day`, partition_granularity: `day`, build_range_start: { sql: `SELECT '2020-01-01'` }, build_range_end: { sql: `SELECT '2022-05-30'` } } } }) ``` ### Batch and streaming data In this scenario, batch data is comes from one pre-aggregation and real-time data comes from a [streaming pre-aggregation][streaming-pre-agg].
Lambda pre-aggregation batch and streaming diagram
You can use lambda pre-aggregations to combine data from multiple pre-aggregations, where one pre-aggregation can have batch data and another streaming. Cube serves each date range with the first rollup in the list that has a fully built partition for it. Each next rollup is used only after the last partition served by the previous one. Partitions of the last rollup are used even if they are not completely built. Build ranges of the referenced rollups have to overlap, see [below](#overlapping-build-ranges). ```yaml title="YAML" theme={null} cubes: - name: streaming_users # This cube uses a streaming SQL data source such as ksqlDB # ... pre_aggregations: - name: streaming type: rollup measures: - CUBE.count dimensions: - CUBE.name time_dimension: CUBE.created_at granularity: day, partition_granularity: day - name: users # This cube uses a data source such as ClickHouse or BigQuery # ... pre_aggregations: - name: batch_streaming_lambda type: rollup_lambda rollups: - users.batch - streaming_users.streaming - name: batch type: rollup measures: - users.count dimensions: - users.name time_dimension: users.created_at granularity: day partition_granularity: day build_range_start: sql: SELECT '2020-01-01' build_range_end: sql: SELECT '2022-05-30' ``` ```javascript title="JavaScript" theme={null} // This cube uses a streaming SQL data source such as ksqlDB cube("streaming_users", { // ... pre_aggregations: { streaming: { type: `rollup`, measures: [CUBE.count], dimensions: [CUBE.name], time_dimension: CUBE.created_at, granularity: `day`, partition_granularity: `day` } } }) // This cube uses a data source such as ClickHouse or BigQuery cube("users", { // ... pre_aggregations: { batch_streaming_lambda: { type: `rollup_lambda`, rollups: [users.batch, streaming_users.streaming] }, batch: { type: `rollup`, measures: [users.count], dimensions: [users.name], time_dimension: users.created_at, granularity: `day`, partition_granularity: `day`, build_range_start: { sql: `SELECT '2020-01-01'` }, build_range_end: { sql: `SELECT '2022-05-30'` } } } }) ``` ## Overlapping build ranges A partition that isn't fully built is skipped for every rollup except the last one. When the build range of a rollup starts exactly where the previous one ends, the partition that has just become complete, for example the previous day right after midnight, is served by no rollup until it's rebuilt. Such days are missing from query results without an error. To avoid that, start the build range of each rollup earlier than the build range of the previous one ends, with a margin longer than the previous rollup's `refresh_key` interval plus its build time. Rows are not counted twice: a rollup skips partitions that the previous one already serves. ```yaml title="YAML" theme={null} cubes: - name: orders # ... pre_aggregations: - name: lambda type: rollup_lambda rollups: - CUBE.batch - CUBE.hot - name: batch # ... partition_granularity: day build_range_end: sql: SELECT CURRENT_DATE - INTERVAL '14 days' refresh_key: every: 1 day - name: hot # ... partition_granularity: day # Starts 2 days before `batch` ends build_range_start: sql: SELECT CURRENT_DATE - INTERVAL '16 days' build_range_end: sql: SELECT CURRENT_DATE refresh_key: every: 10 minute ``` ```javascript title="JavaScript" theme={null} cube(`orders`, { // ... pre_aggregations: { lambda: { type: `rollup_lambda`, rollups: [CUBE.batch, CUBE.hot] }, batch: { // ... partition_granularity: `day`, build_range_end: { sql: `SELECT CURRENT_DATE - INTERVAL '14 days'` }, refresh_key: { every: `1 day` } }, hot: { // ... partition_granularity: `day`, // Starts 2 days before `batch` ends build_range_start: { sql: `SELECT CURRENT_DATE - INTERVAL '16 days'` }, build_range_end: { sql: `SELECT CURRENT_DATE` }, refresh_key: { every: `10 minute` } } } }) ``` If a rollup uses a coarser `partition_granularity` than the next one, the next rollup has to start before the beginning of the previous rollup's last partition, because that whole partition is skipped until it's complete. For example, with `month` partitions on `batch` and `day` partitions on `hot`, start `hot` at `date_trunc('month', CURRENT_DATE - INTERVAL '16 days')`. [streaming-pre-agg]: /docs/pre-aggregations/using-pre-aggregations#streaming-pre-aggregations This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.