What do we most often use together with aggregation queries?

MapReduce.

MapReduce is a programming paradigm and data processing model that works well with aggregation queries in distributed systems.

Map phase:

• Applied to each input data element.
• Creates intermediate key-value pairs.

Reduce phase:

• Groups intermediate values by key.
• Aggregates values for each key (summing, counting, averaging, etc.).

In the context of aggregation queries, the Map phase can:

• Filter data.
• Transform formats.
• Extract fields necessary for aggregation.
• Generate keys for grouping.

The Reduce phase performs the actual aggregation.

For example, to count the number of users by city:

1. Map: For each user, generate a pair (city, 1).
2. Reduce: Sum all the 1s received from the Map phase for each city.

MapReduce enables efficient aggregation over large data volumes in clusters. Modern distributed computing frameworks like Apache Spark and Apache Hadoop provide implementations of MapReduce or its equivalents, optimized for aggregation operations.

Alternatives and additions:

• GROUP BY in SQL: A classic approach for aggregation in relational databases.
• Aggregation pipelines in NoSQL DB (e.g., MongoDB Aggregation Framework): Offer more flexible capabilities for complex aggregations in document-oriented databases, often using concepts similar to MapReduce and data processing pipelines.
• Direct stream processing: For real-time data aggregation, streaming platforms (Apache Kafka Streams, Apache Flink) perform incremental aggregation as data arrives.