Exadata

martin.bach's picture

How important is a Disaster Recovery site for you?

I regularly read threads on the oracle-l mailing list, and occasionally feel very tempted to reply to one. Just recently I saw one that I liked a lot. It is specifically about using an Oracle Database Appliance (ODA) as a Disaster Recovery (DR) solution for an Exadata system. The Exadata configuration was not specified, I assume it was a smaller (eighth rack/quarter rack) configuration.

There were lots of arguments pro and against that Exadata->ODA architecture, and that leads to a broader question: how important is DR for your organisation? This blog post is about my personal experience, and probably strongly influenced by where I live in work (Europe), yours might be different.

About the original discussion

tanelpoder's picture

Where does the Exadata storage() predicate come from?

On Exadata (or when setting cell_offload_plan_display = always on non-Exadata) you may see the storage() predicate in addition to the usual access() and filter() predicates in an execution plan:

SQL> SELECT * FROM dual WHERE dummy = 'X';

D
-
X

Check the plan:

Jonathan Lewis's picture

12c pq_replicate

Another day, another airport lounge – another quick note: one of the changes that appeared in 12c was a tweak to the “broadcast” distribution option of parallel queries. I mentioned this in a footnote to a longer article a couple of months ago; this note simply expands on that brief comment with an example. We’ll start with a simple two-table hash join – which I’ll first construct and demonstrate in 11.2.0.4:

glennfawcett's picture

Analyzing IO at the Cell level with cellcli… a new and improved script

Recently I had the pleasure of corresponding with Hans-Peter Sloot.  After looking at my simple tool in this post to gather cell IO data from cellcli, he took it a several steps further and created a nice python version that goes to the next level to pull IO statistics from the cells.

current_rw_rq.py

This script provides breaks down the IO by “Small” and “Large” as is commonly done by the Enterprise manager.  It also provides a summary by cell.  Here is a sample output from this script.

martin.bach's picture

HCC error on Exadata after partitioning maintenance

Recently I have been asked to investigate the following error on an Exadata system.

ORA-64307: hybrid columnar compression is not supported for tablespaces on this storage type

Well, that’s simple I thought! Must be (d)NFS mounted storage, right? Everyone knows that you can have HCC on Exadata (and a few other storage products). So I looked at the problem and soon found out that the data files in question all resided on the cells. Here is the sequence of events:

tanelpoder's picture

cell flash cache read hits vs. cell writes to flash cache statistics on Exadata

When the Smart Flash Cache was introduced in Exadata, it was caching reads only. So there were only read “optimization” statistics like cell flash cache read hits and physical read requests/bytes optimized in V$SESSTAT and V$SYSSTAT (the former accounted for the read IO requests that got its data from the flash cache and the latter ones accounted the disk IOs avoided both thanks to the flash cache and storage indexes). So if you wanted to measure the benefit of flash cache only, you’d have to use the cell flash cache read hits metric.

tanelpoder's picture

Hard Drive Predictive Failures on Exadata

This post also applies to non-Exadata systems as hard drives work the same way in other storage arrays too – just the commands you would use for extracting the disk-level metrics would be different.

I just noticed that one of our Exadatas had a disk put into “predictive failure” mode and thought to show how to measure why the disk is in that mode (as opposed to just replacing it without really understanding the issue ;-)

tanelpoder's picture

When do Oracle Parallel Execution Slaves issue buffered physical reads – Part 2?

In the previous post about in-memory parallel execution I described in which cases the in-mem PX can kick in for your parallel queries.

A few years ago (around Oracle 11.2.0.2 and Exadata X2 release time) I was helping a customer with their migration to Exadata X2. Many of the queries ran way slower on Exadata compared to their old HP Superdome. The Exadata system was configured according to the Oracle’s “best practices”, that included setting the parallel_degree_policy = AUTO.

tanelpoder's picture

When do Oracle Parallel Execution Slaves issue buffered physical reads – Part 1?

This post applies both to non-Exadata and Exadata systems.

fritshoogland's picture

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.

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