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`Merge` combinators are specifically designed for working with pre-aggregated data. This is particularly useful when dealing with distributed data or when you need to aggregate results from different sources. The `Merge` combinators are optimized to efficiently merge these pre-calculated values, saving you a ton of processing time. This is really useful if you're working with massive datasets. The `Merge` combinators typically work by taking the pre-aggregated results as input and combining them to produce a final aggregated value. For example, the `sumMerge` combinator is designed to sum pre-aggregated sums. You can use this if you have the sum of a value calculated in multiple places (say, different servers or different time periods) and you want to combine them into one final sum. When dealing with distributed systems, `Merge` combinators are extremely efficient since they avoid the need to transfer the raw data. Instead, they can aggregate the pre-calculated results, which reduces network overhead and significantly speeds up query execution. This approach is highly optimized for performance, especially when you need to aggregate large amounts of data. This capability is absolutely crucial for any system that deals with distributed or pre-aggregated data. These combinators are your friends.
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