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Read Heavy vs Write Heavy System
A news website serves about 100 page reads for every new article or comment that is written. A metrics pipeline does the opposite. It stores about 1,000 new measurements for each dashboard read.
Both systems store data. But they need almost opposite designs. So before choosing a database or a caching plan, answer one question: is the traffic mostly reads, or mostly writes?
This lesson explains how to design for each kind of traffic, and why some databases handle writes much better than others.
Two Kinds of Workload
- A read-heavy system serves far more reads than writes
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anmoldeep1509
· 6 months ago
Write-Ahead Logging (WAL) is given as a strategy to improve write performance, but I think it's the opposite. Because in write-ahead logging, we first write to the cache and then to the database. Hence, writing to cache is synchronous and adds time to the write operations.
Instead WAL is a strategy that improves the read performance, because in this case read operations are from the cache (which is much faster then database read).
basaranbahadir
· 2 years ago
Optimize database schema and indexes to improve write performance.
stated in Section Designing for Write-Heavy System.
I guess you explained the opposite in the previous course, by saying that creating an index is beneficial for read-heavy databases, not for write-heavy ones because when any update is requested on data, the index should be updated as well, hence it increases the latency.
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