The document discusses in-memory databases, highlighting their performance benefits compared to traditional storage by running entirely in memory, although with limitations on persistence and data dynamics. It outlines the query execution process in PostgreSQL, detailing the stages from parsing to executing a user's query. Additionally, it provides tuning tips for enhancing PostgreSQL's in-memory performance, such as disabling fsync and utilizing a ramdisk.

1. In-Memory Database
Is it really faster?
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4. What is In-Memory Database?
❏ It runs on the memory only,
never store data in the
physical disk/storage
❏ It is not persistent.
❏ Data is not really dynamic,
since the update in in batch.
❏
❏ It runs on the memory and
store data in the physical
disk/storage also.
❏ It is Persistent
❏ Data is also dynamic, there
are synchronization in
almost realtime.

5. How PostgreSQL actually run your Query
● In Parser, Query is parsed then raw parse tree (nodes)
is generated and analyzed into a query tree, if there are
some query can be rewrite, transform query tree using
rule system into more efficient form (ex: OR rewritten
into UNION).
● Planner will estimate the cheapest path for the
execution.
● Executor, the one really executes a user query into a
real tuple result (if expected).

6. What is In-Memory Database?

7. What is Memory Pool?
➔ A Working Memory for the
Executor/Planner database to
sort and filter page data into a
result.
➔ A Share Cache from Postgres
processes,
Memory for your OS to work,
and Kernel Cache for File
System
Memory for your PostgreSQL
Instance and Heap (Process
local memory)
Memory for Shared Buffer,
Memory Pool

8. How to Tune PostgreSQL into
In-Memory Performance
★ Fsync Off
★ Use RAMDISK and drop indexes
★ Or Use NVMe Disk for PG Wal

10. Let’s have a Demo

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