Apache Pinot is a real-time OLAP datastore designed for high-speed analytical queries. While it excels in fast aggregations and filtering, it does not support joins or nested queries. This post helps you to understand Pinot’s query capabilities and how to work around its limitations.
1. Query Limitations in Apache Pinot
· No Joins or Nested Queries: Unlike traditional SQL databases, Pinot does not support joins and nested queries.
· Alternative: If your use case requires joins, you can integrate Apache Presto with Pinot to enable complex queries.
2. Supported Query Features
2.1 Transformation Functions
Pinot provides various built-in functions for data transformation:
· Math Functions: ADD, SUB, MULTIPLY, etc.
· String Functions: UPPER, LOWER, TRIM, etc.
· Date-Time Functions: TIMECONVERT, DATETIMECONVERT, etc.
· JSON Functions: JSONEXTRACTKEY, JSONFORMAT, etc.
· Binary & Multi-Value Functions: Efficient handling of complex data types.
2.2 Aggregation Functions
Pinot supports fast aggregations for analytics, including:
· COUNT, MIN, MAX, SUM, AVG, and PERCENTILE.
· These functions help derive insights from large datasets in real time.
3. When to Use Apache Pinot for Queries?
· When you need sub-second query responses for large datasets.
· When queries involve fast filtering and aggregations rather than complex joins.
· When working with event-driven data, such as clickstream analytics and monitoring dashboards
In summary, Apache Pinot is designed for fast analytics but lacks support for joins and nested queries. By leveraging transformation and aggregation functions, you can perform efficient analytical queries. For more advanced querying, integrating with Presto is a recommended approach.
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