If you Google for this you’ll find that the easiest way to provide a full audit of everything that happened in your database is to create a duplicate of each table you need history and insert a copy of the record you are changing in there for every update.

For example if you have a table called client

You will also create a table called client_history

You’ll then attach an onUpdate trigger to the client table inserting all the data in your history table. This solution gives you the ability to keep track of everything that happens in your database, when it happened and who-done-it.
What this solution doesn’t deal with is schema changes. Also querying the state of a record at a point in time to join it with other tables might be a bit tricky.

Let’s start from the first point. Schema changes.
The most obvious and simple solution is to put in place a proper process for all database schema changes. i.e. whoever touches the schema is also in charge of updating the history table structure and the trigger on the main table. Doable but a bit too prone to human error if you ask me.

What I generally do is use a small function/stored procedure to create the history on a table. This procedure is in charge of both creating the history table the first time it’s called and updating the structure if the main table has changed.
In this example I’m going to use PostgreSQL. First because it’s a database I’m familiar with and more importantly being open-srouce you can just download it and try this yourself.

My example here is a bit verbose but it serves its purpose. There are many database-specific instructions you could use to extract a table structure or perform some of the simple tasks of this function. However, what I’m trying to demonstrate is the concept and not PostgreSQL trickery.
Most relational databases store the schema information in internal tables (pg_attribute, pg_class and pg_namespace in this case) so that’s what we are going to use to read the structure of our original table.

You’ll notice this function only adds fields to the history table. That’s because even if we remove a field from the original table we still want to keep track of it in our history.

Now on to querying the history to retrieve the state of a record at a specific date.

This is a bit tricky. At the moment as far as I know only Oracle has the ability return a “virtual table” from a function. PostgreSQL can return a RECORD variable – which is great to use inside functions but once outside it loses its structure and turns into a comma separated list of values – or a %ROWTYPE you can define.
This unfortunately means that you’d have to create a custom function for each history table to returns a SET OF client – in our case.

What we are trying to achieve is something like what this query does.

Basically the next record in the history after the time specified.

At this point so far as I can see we have two options. Either create a function that returns a specific type of ROW, or write a more generic history function to return only the id of the history record we need and not the whole row (If you look at the history creation function we are putting a histid column in there).

You could use this function in your queries to get the history record id out like this.

I’m sure you can figure out the rest.

Be careful this is very slow on large data sets. If you are planning to work on millions of records then you should consider building a history lookup function for each table either defining data types for your RECORD or using OUT variables.

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Not just because it’s vital for your business.
For most production systems the speed-bottleneck lies with accessing your data. Database are excellent for storing all your data and keeping it organised. However, when it comes to getting it out quickly, especially if you have lots of it, they are not the sharpest of tools.

That’s why I always try to press home the importance of keeping on top of your data.

Even though you think all your data is neatly organised in your perfectly structured database it keeps changing shape, or rather the understanding your database has of your data keeps changing.
For example in PostgreSQL the “shape” of your data is stored in a table called pg_statistics. Data is collected and stored there by analyze.

The query planner uses the data collected in pg_statistics to pick the most efficient way to run your queries. Unfortunately no system is perfect, even the best planner makes mistakes. You have to strike a balance between letting analyze collect as much data as possible to give your database a better understanding of your data and keeping it slim enough for it to be quick.

So as much as you can trust machines I suggest you try to keep on top of your data yourself.

There’s a couple of very simple ways to do that.

First. Keep an eye on your database logs, exactly like you do with your webserver logs. There’s a few open-source applications that can help you do that like Enterprise Postgres Query Analyser.
This will give you a basic understanding of which query/ies you will have to focus on.

Once you have an idea of which ones are the slowest queries you should keep an eye on the explain plan at regular intervals. In PostgreSQL I have a scheduled job that every day runs “EXPLAIN ANALYZE” on the heaviest queries in my system and compares the output with the previous day’s.

It’s a lot of work and you are probably better off confronting these problems as they come up and not waste valuable development time creating the most optimised database ever.
Setting up auto-vacuuming properly will keep you safe for a long while.

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Maybe that’s a bit too harsh, maybe recursive query are not evil, it’s just the people who use them.

I spend quite a lot of time working with PostgreSQL users helping them optimise their queries. When I read that PostgreSQL 8.4 added support for recursive queries I knew that a whole new hellish chapter in my life would begin.

First off. What are recursive queries: (from PostgreSQL manual)

Recursive queries are typically used to deal with hierarchical or tree-structured data. A useful example is this query to find all the direct and indirect sub-parts of a product, given only a table that shows immediate inclusions:

WITH RECURSIVE included_parts(sub_part, part, quantity) AS (
SELECT sub_part, part, quantity FROM parts WHERE part = ‘our_product’
UNION ALL
SELECT p.sub_part, p.part, p.quantity
FROM included_parts pr, parts p
WHERE p.part = pr.sub_part
)
SELECT sub_part, SUM(quantity) as total_quantity
FROM included_parts
GROUP BY sub_part

These structures are commonly used in relational database. Just think about a threaded comment system for a blog or an industry classification for securities on multiple levels (financial data is what I’m most familiar with).

In this latter case you can image that you’ll hardly ever extract industry classification information by itself. It’s generally used as a sub-query to provide additional information about a security, a trade or what have you.

As I said earlier I have nothing against recursive queries per se. However, I can already see people out there creating monster-queries in production systems. The sort of monster query that needs to be executed 50 times a second, the one that just doesn’t work.

Storing and retrieving tree-structured data in SQL is one of my favourite questions in interviews. I always make a point of asking it. Not because it’s particularly challenging technically but because it will tell me a lot about the way the person I’m interviewing thinks about data.

The first part of the question is obviously do design a structure to hold threaded blog comments.

Whether you use a separate table to hold the relationship between nodes or a self-referencing parent id column in the same table I don’t really care. So long as you come up with an answer we can move on with the interview, because the answer to the next part of the question is what interests me.

I will now call your blog page with the ID from an element in your structure, any element. I want you to return instantly the ID of the root element for that branch of the tree.

- Root comment 1
   - Child 1.1
   - Child 1.2
      - Child 1.2.1
   - Child 1.3
- Root comment 2
   - Child 2.1
      - Child 2.1.1
         - Child 2.1.1.1
   - Child 2.2

I will call you with 2.1.1.1 and I want you to tell me 2, instantly. Feel free to change your database structure.

Their answer to this will tell me how they feel about de-normalisation and if they can think in those terms. We are talking about the daft requirements written by a product person who’s clearly gone quite mad. All he cares about is getting the data out quickly, nothing else.

Easiest de-normalised way out is to add a root id column in each comment row. It will make inserting new comments slower but it won’t require any recursion to go back to the top when selecting data.

If all you can come up with is recursive query I’ll be sorely disappointed. It’s cool and elegant but not nearly efficient enough for a high-availability production system.

Feel free to talk about recursive queries when I ask you this question, just remember to put the magic words “materialized view” in front of it. then we can talk.

Let this bet a warning to you. If I find a non-materialized/cached recursive query in your production code I will recursively kick you in the head.

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Last week I wrote a rather long post asking for suggestions on how to cache and access a time series stored in an array in memory.

After some more research I have decided that the solution proposed by Uzair in his comment to the original post with some minor tweaks.

My originial idea involved creating a map in memory with one entry for each point in my series and then lookup the item requested in there.
This is very efficient when requests come in for a time which is in my index, if the point is not in there I’d have to run a binary search on the whole index. Which as Anon pointed out in his comment is still very fast.

if your ordered index is in-memory.

even if you had 1TB of memory (quite a lot), and each memory access took you 1 microsecond (slow),

each search would only take worse case of 40 accesses (2^40 = 1 trillion)
which would be 40 microseconds. this is not very long at all.

assuming some more reasonable ball-park figures of 4GB of memory, and
200 nanosecond access times, gives you

32 accesses * 200 nanos = 6 microseconds

this is over 150,000 searches per second.

The index will be a map of maps. Basically I’ll distribute a number of entries in my index at regular intervals. Each entry will contain a map of entries that preceded it in my time series.
Since I will know the first and last point in the series and the distribution of my regular entries *z* when a request *x* comes in I will be able to work out where is my next regular index entry *y*)

y = x – mod(x/z) + z

Once I have retrieved the map contained in my regular entry in the index I can proceed and lookup the value requested in there. If it’s not a point in my map I will still have to do a binary search but on a much smaller dataset – which I should be able to work on locally without having to rely on memcached over the network. The only “downside” of this solution is that I’ll have to make at least 2 lookups in my index.

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This is something I’ve been thinking about for a while. How can I cache and have nearly-instant access to any data point within the time series without having to splash out on expensive time series databases. I have an half cooked solution and decided to write this post to see if anybody out there has worked on a similar problem and has a better solution to share.

Specifically I’m trying to work with financial data. Take for example transactions. A position management system needs to be able to store millions of a transactions for a number of portfolios. Storing the data is not a problem for any relational database – The problem is querying it.

Financial data is, by its own nature, relational. This means, as far as I’m concerned, that you’d always want to store your raw data in a relational model without de-normalizing it.

Now this is all good and well but when we need to access the transactions for a specific portfolio between the first of January 1999 and the 31st of December 2008 with all the related security information and prices relational databases start to struggle. Of course it is doable, but if you can get the data out first CPU cycles used by your software to do the computation are less expensive than you database’s.

What I want to be able to do is build a cache of the flattened (de-normalized) data and make it available to n clients at the same time.

The obvious solution is to build a massive time-ordered array of all the transactions and store it in memory. Considering the potential size of the array something like memcached would be more appropriate.
Here comes the second problem. Assume a client calls your software with a couple of dates, you will be forced to loop over the whole array to find the chunk you need. Again, doable, but inefficient.

Array is definitely the way to go to store the data. What I need is an “index” which will tell me exactly where a specific transaction is stored in the array.

Here’s a possible solution I’ve been toying with today.

The idea comes from a technique used in videogames called voxel-based-rending and ray-casting. I’ll try and briefly explain what that is (at least as far as my idea is concerned):

Say you are trying to render a landscape. Voxel rendering makes this fairly simple and is achieved column by column. For each column, a maximum Y value was stored (in a Y-Buffer). The pixels of the column in question are filled in starting at the bottom of the screen, only if their Y value was greater than the one stored in the Y-Buffer. This rendering method made the process of removing non-visible parts of the scene very efficient and very easy.

While creating the array we should also create a map (our Y-buffer). The map will simply contain a transaction unique id as key and the position in the array as value.

This way when the client calls our APIs with the last unique identifier it has received we’ll just have to make a very quick lookup in our Y-Buffer for the array position and this will point us exactly to what we need to return.

This could work, but it’s only a partial solution, or rather a solution for a very specific problem. What we really want is for the client to be able to hand in 2 date-time values (from and to).
We could create our index using time information. The problem is that transactions are not at constant time-intervals. Which means that if we are called with a time value that is not in our index we’d still end up looping over the values around the closest point to find the position of the first element in the series.

An alternative would be to build our index using the maximum precision allowed. say an index of milliseconds where the last element position in the array is repeated in the index until a new one appears. Doable but very memory consuming.

This is as far as I’ve gone. Any other ideas out there?

UPDATE: follow-up article

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