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Read Heavy vs Write Heavy System
Two systems can store the same kind of data and still need opposite designs. A news site serves a hundred reads for every write. A metrics pipeline takes in a thousand writes for every read.
So before you choose a database or a caching plan, answer one question. Is the traffic mostly reads, or mostly writes?
A read-heavy system serves far more reads than writes. News sites, product catalogs, social feeds, most public web applications. A write-heavy system takes in far more writes than it serves reads. Logging and metrics pipelines, sensor ingestion, click tracking, chat delivery.
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anmoldeep1509
· 4 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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