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Oracle and postgres disk IO performance

This post is about one of the fundamentally important properties of a database: how IO is done. The test case I studied is doing a simple full table scan of a single large table. In both Oracle and postgres the table doesn’t have any indexes or constraints, which is not a realistic example, but this doesn’t change the principal topic of the study: doing a table scan.

I used a publicly available dataset from the US bureau of transportation statistics called FAF4.5.1_database.zip
The zipped file is 347MB, unzipped size 1.7GB.

Where does the log writer spend its time on?

The Oracle database log writer is the process that fundamentally influences database change performance. Under normal circumstances the log writer must persist the changes made to the blocks before the actual change is committed. Therefore, it’s vitally important to understand what the log writer is exactly doing. This is widely known by the Oracle database community.

The traditional method for looking at log writer performance is looking at the wait event ‘log file parallel write’ and the CPU time, and comparing that to the ‘log file sync’ alias “commit wait time”. If ‘log file parallel write’ and ‘log file sync’ roughly match, a commit is waiting on the log writer IO latency, if it isn’t then it’s unclear, and things get vague.

Oracle 12.1 big table caching IO code path

Recently I was triggered about the ‘automatic big table caching’ feature introduced in Oracle version 12.1.0.2 with Roger Macnicol’s blogpost about Oracle database IO and caching or not caching (https://blogs.oracle.com/smartscan-deep-dive/when-bloggers-get-it-wrong-part-1 https://blogs.oracle.com/smartscan-deep-dive/when-bloggers-get-it-wrong-part-2). If you want to read something about the feature in general, search for the feature name, you’ll find several blogposts about it.

The full table scan direct path read decision for version 12.2

This post is about the decision the Oracle database engine makes when it is using a full segment scan approach. The choices the engine has is to store the blocks that are physically read in the buffercache, or read the blocks into the process’ PGA. The first choice is what I refer to as a ‘buffered read’, which places the block in the database buffercache so the process itself and other processes can bypass the physical read and use the block from the cache, until the block is evicted from the cache. The second choice is what is commonly referred to as ‘direct path read’, which places the blocks physically read into the process’ PGA, which means the read blocks are stored for only a short duration and is not shared with other processes.

Oracle 12c in-memory option and IO

This article is about the Oracle 12c in-memory option, and specifically looks at how the background worker processes do IO to populate the in-memory column store.

Hardware: Apple Macbook with VMWare Fusion 7.1.3.
Operating system: Oracle Linux 6.7, kernel: 3.8.13-118.el6uek.x86_64.
Database version: Oracle 12.1.0.2
Patch: opatch lspatches
19392604;OCW PATCH SET UPDATE : 12.1.0.2.1 (19392604)
19303936;Database Patch Set Update : 12.1.0.2.1 (19303936)

But first things first, let’s setup the in-memory option first with a test table. The first thing to consider is to create the in-memory area to store the objects. I only want a single table stored in the in-memory area, so I can very simply look at the size of object:

Direct path and buffered reads again: compressed tables.

In the previous post on the decision between buffered and direct path reads I showed the decision is depended on the version. Up to and including version 11.2.0.2 the size of a segment needs to be five times small table threshold in order to be considered for direct path reads, and starting from 11.2.0.3 the database starts considering direct path reads starting from small table threshold. The lower limit just discussed is small table threshold or five times small table threshold with lower versions, upper limit is called “very large object threshold” (VLOT) and is five times the size of the buffercache, which is the threshold after which a table scan always is going via direct path.

Physical IO on Linux

I posted a fair amount of stuff on how Oracle is generating IOs, and especially large IOs, meaning more than one Oracle block, so > 8KB. This is typically what is happening when the Oracle database is executing a row source which does a full segment scan. Let’s start off with a quiz: what you think Oracle is the maximum IO size the Oracle engine is capable of requesting of the Operating System (so the IO size as can be seen at the SCI (system call interface) layer? If you made up your answer, remember it, and read on!

The real intention of this blogpost is to describe what is going on in the Oracle database kernel, but also what is being done in the Linux kernel. Being a performance specialised Oracle DBA means you have to understand what the operating system does. I often see that it’s of the utmost importance to understand how an IO ends up as a request at the NAS or SAN head, so you understand what a storage admin is talking about.

Oracle IO wait events: db file sequential read

(the details are investigated and specific to Oracle’s database implementation on Linux x86_64)

Exadata IO: This event is not used with Exadata storage, ‘cell single block physical read’ is used instead.
Parameters:
p1: file#
p2: block#
p3: blocks

Despite p3 listing the number of blocks, I haven’t seen a db file sequential read event that read more than one block ever. Of course this could change in a newer release.

Linux strace doesn’t lie after all.

strace is a linux utility to profile system calls. Using strace you can see the system calls that a process executes, in order to investigate the inner working or performance. In my presentation about multiblock reads I put the text ‘strace lies’. This is NOT correct. My current understanding is that strace does show every system call made by an executable. So…why did I make that statement? (editorial note: this article dives into the inner working of Linux AIO)

Exadata: disk level statistics

This is the fourth post on a serie of postings on how to get measurements out of the cell server, which is the storage layer of the Oracle Exadata database machine. Up until now, I have looked at the measurement of the kind of IOs Exadata receives, the latencies of the IOs as as done by the cell server, and the mechanism Exadata uses to overcome overloaded CPUs on the cell layer.