![]() Let's use four tables to demonstrate this example: machines, sensors, sensor_readings and real_sensor_readings. For queries that require final aggregation of the query results processing, such as 'order by with limit' clause, achieved through the pull-to-coordinator approach.Ĭonsider an IoT scenario related to Industrial IoT (IIoT) in a manufacturing setting. This can be accomplished through techniques like repartitioning. In certain cases, queries may involve joining on columns that are not distributed, which necessitates on-the-fly matching of source and target rows. Beyond the Basics: Exploring MERGE in Complex Scenarios However, this limits flexibility for both the data location and join clauses in the MERGE statement. ![]() This approach has minimal overhead in terms of distributed query planning time and network traffic, which enables high concurrency and low latency. Query routing allows Citus to support all the SQL functionality of the underlying PostgreSQL servers at scale for multi-tenant (SaaS) applications, which typically filter by tenant ID. Figure 2: Identify the potential shard(s) for modification by the MERGE, push down the MERGE command on the individual shards, and allow worker node(s) execute MERGE independently on shard-tables. Query routing means taking (part of) the query and letting the worker node that stores the relevant shards handle the query, which is possible when the query filters and joins by the distribution column. Since the data pertaining to a customer is stored on a singular worker node, the MERGE command uses query routing. order_id WHEN NOT MATCHED THEN - New entry, record it in target INSERT ( customer_id, last_order_id, order_center, order_count, last_order ) VALUES ( customer_id, s. order_center = 'New York' THEN - Remove the customer in target DELETE WHEN MATCHED THEN - Existing customer, update the order count UPDATE SET order_count = t. MERGE INTO customers t USING orders s ON ( t. From theory to practice: Diving into use case(s) Let's start by looking at a simple query-routing scenario Note: Each output row of the join can activate at most one when-clause. The corresponding action is then applied, and processing continues for the next row.WHEN clauses are evaluated in the exact specified order until one of them is activated.The first clause that evaluates to true is the one (and the only one) that gets executed. The status of MATCHED or NOT MATCHED is assigned to each row just once, after which the WHEN clauses are evaluated in the order they are written. How MERGE works in Postgres (as of Postgres 15) Figure 1: MERGE command performs a left outer join between the source-table and target-table. Batch operations: When performing bulk operations that may involve both insertions and updates, using MERGE can be more efficient than issuing separate insert and update commands, as it minimizes the number of total commands sent and processed.Real-time data update: If you're dealing with real-time data that's constantly being updated, such as stock prices or sensor readings, you can use MERGE to ensure that the most recent data is always available, without the need to distinguish between new and existing data.Logging: In cases where you're logging events to a table and you want to count occurrences of events, and an event occurs that's already in the table, you increment a counter otherwise, you insert a new row.Data deduplication: If you're handling data that might contain duplicates, and a duplicate row is detected, you can simply update the existing record.MERGE ensures that the database remains consistent with the external data source. ![]()
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