Should I worry about 1B+ rows in a table? - mysql

Question

I've got a table which keeps track of article views. It has the following columns:
id, article_id, day, month, year, views_count.
Let's say I want to keep track of daily views / each day for every article. If I have 1,000 user written articles. The number of rows would compute to:
365 (1 year) * 1,000 => 365,000
Which is not too bad. But let say. The number of articles grow to 1M. And as time passes by to 3 years. The number of rows would compute to:
365 * 3 * 1,000,000 => 1,095,000,000
Obviously, over time, this table will keep growing. And quite fast. What problems will this cause? Or should I not worry since RDBM's handle situations like this quite commonly?
I plan on using the views data in our reports. Either break it down to months or even years. Should I worry about 1B+ rows in a table?

Answer 1

The question to ask yourself (or your stakeholders) is: do you really need 1-day resolution on older data?
Have a look into how products like MRTG, via RRD, do their logging. The theory is you don't store all the data at maximum resolution indefinitely, but regularly aggregate them into larger and larger summaries.
That allows you to have 1-second resolution for perhaps the last 5-minutes, then 5-minute averages for the last hour, then hourly for a day, daily for a month, and so on.
So, for example, if you have a bunch of records like this for a single article:
year | month | day | count | type
-----+-------+-----+-------|------
2011 | 12 | 1 | 5 | day
2011 | 12 | 2 | 7 | day
2011 | 12 | 3 | 10 | day
2011 | 12 | 4 | 50 | day
You would then at regular periods create a new record(s) that summarises these data, in this example just the total count for the month
year | month | day | count | type
-----+-------+-----+-------|------
2011 | 12 | 0 | 72 | month
Or the average per day:
year | month | day | count | type
-----+-------+-----+-------+------
2011 | 12 | 0 | 2.3 | month
Of course you may need some flag to indicate the "summarised" status of the data, in this case I've used a 'type' column for finding the "raw" records and the processed records, allowing you to purge out the day records as required.
INSERT INTO statistics (article_id, year, month, day, count, type)
SELECT article_id, year, month, max(day), sum(count), 'month'
FROM statistics
WHERE type = 'day'
GROUP BY article_id, year, month, type
(I haven't tested that query, it's just an example)

Answer 2

The answer is "it depends". but yes, it will probably be a lot to deal with.
However - this is generally a problem of "cross that bridge when you need to". It's a good idea to think about what you could do if this becomes a problem for you in the future.. but it's probably too early to actually implement any suggestions until they're necessary.
My suggestion, if it ever occurs, is to not keep the individual records for longer than X-months (where you adjust X according to your needs). Instead, you'd store the aggregated data that you currently feed into your reports. What you'd do is run, say, a daily script that looks at your records and grabs any that are over X months old... and create a "daily_stats" object of some sort, then delete the originals (or better yet, archives them somewhere).
This will ensure that only X-months worth of data are ever in the db - but you still have quick access to an aggregated form of the stats for long-timeline reports.

Answer 3

It's not something you need to worry about if you can put some practices in place.
Partition the table; this should make archiving easier to do
Determine how much data you need at present
Determine how much data you can archive
Ensure that the table has the right build, perhaps in terms of data types and indexes
Schedule for a time when you will archive partitions that meet the aging requirements
Schedule for index checking (and other table checks)
If you have a DBA in your team, then you can discuss it with him/her, and I'm sure they'll be glad to assist.
Also, like what is used in many data warehouses, and I just saw #Taryn's post (which I agree with -> )store aggregated data as well. This is quickly suggested based on the data you keep in the involved table. If you have trouble with possible editing/updating of records, then it brings to light (even more) the fact that you will just have to set restrictions like how much data to keep (which means this data is what can be modified) and have procedures+jobs in place to ensure that the aggregated data is checked/updated daily and can be updated/checked manually when any changes are made. This way, data integrity is maintained. Discuss with your DBA what other approaches you can take...
By the way, in case you didn't already know.. Aggregated data are normally needed for weekly or monthly reports, and many other reports based upon an interval. Granulize your aggregation as needed, but not so much that it becomes too tedious or seemingly exaggerated.