Ingestion Aggregations

Many data analytics use-cases only need aggregated data. For example, data used in charts can be aggregated down to one row per time bucket per dimension combination.

Doing this results in much less storage and better query performance. Configuring this for a table is done via the Aggregation Config in the table config.

Aggregation Config

The aggregation config controls the aggregations that happen during real-time data ingestion. Offline aggregations must be handled separately.

Below is a description of the config, which is defined in the ingestion config of the table config.

{
  "tableConfig": {
    "tableName": "...",
    "ingestionConfig": {
      "aggregationConfigs": [{
        "columnName": "aggregatedFieldName",
        "aggregationFunction": "<aggregationFunction>(<originalFieldName>)"
      }]
    }
  }
}

Requirements

The following are required for ingestion aggregation to work:

• Ingestion aggregation config is effective only for real-time tables. (There is no ingestion time aggregation support for offline tables. We need use Merge/Rollup Task or pre-process aggregations in the offline data flow using batch processing engines like Spark/MapReduce).
• Stream ingestion type must be lowLevel.
• All metrics must have aggregation configs.

• All metrics must be noDictionaryColumns.

• aggregatedFieldName must be in the Pinot schema and originalFieldName must not exist in Pinot schema

Example Scenario

Here is an example of sales data, where only the daily sales aggregates per product are needed.

Example Input Data

{"customerID":205,"product_name": "car","price":"1500.00","timestamp":1571900400000}
{"customerID":206,"product_name": "truck","price":"2200.00","timestamp":1571900400000}
{"customerID":207,"product_name": "car","price":"1300.00","timestamp":1571900400000}
{"customerID":208,"product_name": "truck","price":"700.00","timestamp":1572418800000}
{"customerID":209,"product_name": "car","price":"1100.00","timestamp":1572505200000}
{"customerID":210,"product_name": "car","price":"2100.00","timestamp":1572505200000}
{"customerID":211,"product_name": "truck","price":"800.00","timestamp":1572678000000}
{"customerID":212,"product_name": "car","price":"800.00","timestamp":1572678000000}
{"customerID":213,"product_name": "car","price":"1900.00","timestamp":1572678000000}
{"customerID":214,"product_name": "car","price":"1000.00","timestamp":1572678000000}

Schema

Note that the schema only reflects the final table structure.

{
  "schemaName": "daily_sales_schema",
  "dimensionFieldSpecs": [
    {
      "name": "product_name",
      "dataType": "STRING"
    }
  ],
  "metricSpecs": [
    {
      "name": "sales_count",
      "dataType": "LONG"
    },
    {
      "name": "total_sales",
      "dataType": "DOUBLE"
    }
  ],
  "dateTimeFieldSpecs": [
    {
      "name": "daysSinceEpoch",
      "dataType": "LONG",
      "format": "1:MILLISECONDS:EPOCH",
      "granularity": "1:MILLISECONDS"
    }
  ]
}

Table Config

From the below aggregation config example, note that price exists in the input data while total_sales exists in the Pinot Schema.

{
  "tableName": "daily_sales",
  "ingestionConfig": {
    "transformConfigs": [
      {
        "columnName": "daysSinceEpoch",
        "transformFunction": "toEpochDays(timestamp)"
      }
    ],
    "aggregationConfigs": [
      {
        "columnName": "total_sales",
        "aggregationFunction": "SUM(price)"
      },
      {
        "columnName": "sales_count", 
        "aggregationFunction": "COUNT(*)"
      }
    ]
  }
  "tableIndexConfig": {
    "noDictionaryColumns": [
      "sales_count",
      "total_sales"
    ]
  }
}

Example Final Table

product_name	sales_count	total_sales	daysSinceEpoch
car	2	2800.00	18193
truck	1	2200.00	18193
truck	1	700.00	18199
car	2	3300.00	18200
truck	1	800.00	18202
car	3	3700.00	18202

Allowed Aggregation Functions

function name	notes
MAX
MIN
SUM
COUNT	Specify as COUNT(*)
DISTINCTCOUNTHLL	Not available yet, but coming soon

Frequently Asked Questions

Why not use a Startree?

Startrees can only be added to real-time segments after the segments has sealed, and creating startrees is CPU-intensive. Ingestion Aggregation works for consuming segments and uses no additional CPU.

Startrees take additional memory to store, while ingestion aggregation stores less data than the original dataset.

When to not use ingestion aggregation?

If the original rows in non-aggregated form are needed, then ingestion-aggregation cannot be used.

I already use the aggregateMetrics setting?

The aggregateMetrics works the same as Ingestion Aggregation, but only allows for the SUM function.

The current changes are backward compatible, so no need to change your table config unless you need a different aggregation function.

Does this config work for offline data?

Ingestion Aggregation only works for real-time ingestion. For offline data, the offline process needs to generate the aggregates separately.

Why do all metrics need to be aggregated?

If a metric isn't aggregated then it will result in more than one row per unique set of dimensions.