Quiz Night.

I was on a customer site recently where I needed to add a NOT NULL constraint to a table of 200 million rows – without taking any downtime. It’s not difficult (provided you are happy with a check constraint rather than a column definition.)

alter table t1
	add constraint t1_ck_colX_nn check (colX is not null)
	enable novalidate
;

The first step creates the constraint and enables it – but doesn’t validate it. This means that future data (and changes) will obey the constraint, but there may be illegal data already in the table that will not be checked. You will have an interruption to service doing this, as your session will wait to lock the table in share mode (mode 4) to add the constraint – so will be blocked by current update transactions, and will block new update transactions. In a typical OLTP system this should result in just a brief pause.

Index rebuilds

A couple of years ago I wrote about a poster on the OTN db forum who was puzzled by the fact that when he started rebuilding tables they got bigger.  (Bad luck, sometimes that’s what happens !)

A few days ago a related question appeared: I rebuilt some indexes and my query got slower. (Bad luck, sometimes that’s what happens – again!)

If you rebuild an index it’s physically different and its statistics are different. Plans can change and go slower because the index stats look sufficiently different; plans can stay the same and go slower because the index is physically different. 

I’ve added a couple of comments to the thread – there may still be some further mileage in it.

[Further reading on rebuilding indexes]

Cardinalilty One

I think anyone who has read Wolfgang Breitling’s material about the optimizer will be familiar with the concept of Cardinality Feedback and one particular detail that when Oracle gets a cardinality estimate of one for a “driving” table then there’s a good chance that the execution plan will go wrong. (That’s not rule, by the way, just a fairly common observation after things have gone wrong.)

A recent note on OTN reminded me of a particular scenario where this specific problem can occur. It’s not particularly common, but it may hit people who are building data warehouses from multiple different sources. We start with an unlikely looking data set and very simple query:

Joins – MJ

The final join mechanism in my “all joins are nested loop joins” argument is the Merge Join – a join mechanism that depends on both its row sources being pre-sorted on the join columns. (For a quick reference list of URLs to all three articles in turn, see: Joins.)

Joins – HJ

In the second note on my thesis that “all joins are nested loop joins with different startup costs” I want to look at hash joins, and I’ll start by going back to the execution plan I posted on “Joins – NLJ”. (For a quick reference list of URLs to all three articles in turn, see: Joins.)

Scalability Conflict

Here’s an example of how you have to think about conflicts of interest when dealing with problems of scalability. It starts with a request (that I won’t give in detail, and was a little different from the shape I describe below)  from a client for advice on how to make a query go faster.

Basic problem: the query runs about 20 times per second, returning a very small number of rows; it’s basically a very simple “union all” of three query blocks that access the same table in slightly different ways.

Subquery Factoring (3)

From time to time I’ve warned people that subquery factoring should be used with a little care if all you’re trying to do is make a query more readable by extracting parts of the SQL into “factored subqueries” (or Common Table Expressions – CTEs – if you want to use the ANSI term for them). In principle, for example, the following two queries should produce the same  execution plan:

Coalesce

The following question came up in an email conversation a little while ago:

Are you aware of any problems a large oltp site might have with running index coalesce during production hours, as opposed to doing index rebuilds in a maintenance window?

Testing

The old chestnut of “optimal block size” came up on OTN again a few weeks ago, with someone asking for advice on how to do some testing to decide on the optimal block size for a database. The correct answer to this question is you don’t: you assume you are going to use the default size for your platform and then think about whether there are any very specific jobs that your application does that might gain some sort of worthwhile benefit if you used a non-default size.

Nevertheless, the OP came back some time later with a few results which suggested that some of his tests showed that a 4KB block size gave significantly better performance than the same tests using 8KB and 16KB block sizes.

But there’s a problem with the conclusion. If you examine the results carefully, and think about what type of work must happen in the tests, you realise that this particular test was not about the blocksize – it was about the network and the client program. (I haven’t included a link to the posting where I explained this – it’s just a little later in the same thread. This is just to give you the option of working out why the test is wrong before you read my comments about it.)

Update 18th Aug 2010

The investigation continues – with the OP comparing the resultsof using a table with a single 2000 byte column to a table with many columns with an similar total size. Again, though, the anomaly in timing he is chasing seems to be about network traffic time, NOT about database block size.

(I’ve only sent one reply to this thread at the moment, but the OP has been good at supplying extra data in the past, so the discussion may evolve to produce further interesting information.)

double trouble

In the latest Quiz Night, I asked how you could make a query more efficient by changing a two table join into a three table join – with the clue that my third table was a repeat of the first table. Gary Myers, in comment 4,  provided the type of answer I was looking for. Sometimes it is more efficient to get a small amount of data from a table on a first pass then go back and get the rest of the data on a second pass – especially if the first pass is an ‘index only’ operation.

Contention

There was a news item in the UK last week about a man in Chideock, Dorset who staged a protest about the volume of heavy traffic that has to come through the village where he lives.

A pedestrian crossing has recently been installed on the road, using traffic-light control. So one morning he decided to cross the road, and then come back again, non-stop, for an hour. Each time he got across the road he pressed the crossing control button to come back. A few vehicles got through on the green light each time, but after just one hour he had caused a four mile tailback of traffic.

Let this be a lesson to Oracle DBAs and developers – even a small job, if repeated very frequently, can create havoc with your system.

Quiz Night

I have two queries designed to return the same result set in the same order. In outline they look like this (look carefully at the from clauses):

select  ...
from    tableA  t1,
        tableB  t2
where
        t1.filter = ...
and     t2.join = t1.join
and     t2.filter = ...
order by ...


select  ...
from    tableA  t1,
        tableB  t2,
        tableA  t3
where
        t1.filter = ...
and     t2.join = t1.join
and     t2.filter = ...
and     ...  -- to be continued
order by ...

How did I manage to take the first query and make it more efficient by turning it from a two-table join to a three-table join ?

SQL*Net compression

Here’s a little demonstration I’ve been meaning to write about for the last few years – it’s very simple: create a table, then query it a couple of times.

Failed Login

Here’s a piece of code I found recently running every half hour on a client site:

SQL_ID = 2trtpvb5jtr53
SELECT
        TO_CHAR(current_timestamp AT TIME ZONE :"SYS_B_0", :"SYS_B_1") AS curr_timestamp,
        COUNT(username) AS failed_count
FROM
        sys.dba_audit_session
WHERE
        returncode != :"SYS_B_2"
AND     TO_CHAR(timestamp, :"SYS_B_3") >= TO_CHAR(current_timestamp - TO_DSINTERVAL(:"SYS_B_4"), :"SYS_B_5")

Index too big

I thought I’d posted this a couple of years ago – but maybe it was something I put on the OTN database forum in response to a question. If it was, the same (or similar) question has recently appeared.  “How come my index is so big when there’s no data in the table ?”