Distributed Query Execution

Mastering Distributed Query Execution in Time-Series Clusters

In this lesson, you will learn how analytical queries are decomposed and executed across a cluster of nodes to process massive time-series datasets efficiently.

When dealing with time-series data at scale, a single node cannot hold all the points or handle the compute load of complex aggregations. To solve this, we use a "scatter-gather" approach to distributed query execution.

The Scatter-Gather Pattern

When a client submits an analytical query—such as calculating the avg() CPU usage across all servers over the last 24 hours—the cluster coordinator node performs three primary steps:

1. Query Decomposition (Scatter): The coordinator identifies which nodes hold the relevant shards of data. If your data is partitioned by host_id or time_range, the query is pruned so that only nodes containing the specific time-series blocks are contacted.
2. Parallel Execution: Each individual node executes the scan and local aggregation. For example, if you are looking for the maximum value in a window, each node calculates the local maximum for its own data partition.
3. Final Aggregation (Gather): The coordinator collects the partial results from the distributed nodes, merges them, and computes the final global result to return to the user.

Illustration of Parallel Processing

Consider a query to find the peak temperature across ten nodes:

SELECT max(temp) 
FROM sensor_data 
WHERE time > '2023-10-01' 
GROUP BY location;

Instead of moving raw data to a central machine, the system pushes the max() logic down to each node:

• Node A returns max = 85
• Node B returns max = 92
• Coordinator receives [85, 92] and performs a final max(85, 92) to return 92.

By performing this "push-down" of aggregations, we minimize network I/O, which is typically the primary bottleneck in distributed systems. The coordinator only handles the final merge step, allowing the heavy lifting of raw data scanning to happen in parallel across the entire cluster.

Key Takeaway: Distributed query execution relies on scattering tasks to nodes where the data resides and gathering summarized partial results to minimize network traffic and maximize parallel compute power.