How do I ensure consistency of aggregates with high availability?

Question

My team needs to find a solution to the following problem:

Our application allows users to view total sales for the enterprise, totals by product, totals by region, totals by region x product, totals by regions x division, etc. You get the idea. There are so many values that need to be aggregated to get many of those totals that they cannot be computed on the fly - we have to pre-aggregate them to provide decent response times, a process that takes about 5 minutes.

The problem, which we thought was a common one but can find no references to, is how to allow updates to various sales without shutting off the users. Also, the users cannot accept eventual consistency - if they drill down on a total of 12 they better see numbers that add up to 12. So we need Consistency + Availability.

The best solution we've come up with so far is to direct all queries to a redundant database, "B" (optimized for queries) while updates are directed to the primary database, "A". When we decide to spend the 5 minutes to update all the aggregates, we update database "C", which is yet another redundant database just like "B". Then, new user sessions get directed to "C", while existing user sessions continue to use "B". Eventually, warning anyone left using "B", we kill the sessions on "B" and re-aggregate there, swapping the roles of "B" and "C". Typical drain-stop scenario.

We are surprised that we cannot find any discussion of this and are concerned that we are over-engineering this problem or maybe it's not the problem we think it is. Any advice is greately appreciated.

Answer 1

This was an interesting problem so I thought about it on the train, and I came up with the idea of storing a timestamp for each row in the database that you aggregate over. (I think this technique has a name, but it escapes me and googling isn't finding it...)

The timestamp would indicate when this row was inserted. In addition:

-If rows can be updated, then you will have two 'versions' of the row at once, one more recent than the other.

-If rows can be deleted, then there will need to be a 'deleted version' row that specifies when it was deleted.

Now you can do things such as:

1) Say you update the aggregates at Jan 1 2000 midnight. You can have views of the table return the table's data as though it was Jan 1 2000 midnight, ignoring all inserts/updates/deletes more recent than that. Now the aggregates are as up to date as the data in the view AND you can keep adding data to the underlying table.

2) I don't know how feasible/easy to guarantee it's reliable this would be, but you could have 'differentially computed aggregates' where on Jan 2 2000 midnight, you take the aggregates of Jan 1 2000 midnight and update them only with the data that has been changed since that time - saving you from recomputing so much historical data. (Of course, it gets hairier once you consider rows being updated or deleted that are older than 24 hours)

3) Whenever you bring your aggregates up to date, you can merge updated and deleted rows with their older version and get rid of the older version, so you only have to keep duplicates of rows around when you need them to separate rows that have been aggregated and rows that aren't (this also means that, for instance, if all your aggregates run at once, and you update a row three times in quick succession, you only need to keep the most recent update-indicating row)

Answer 2

If updates cannot be computed on the fly, then caching of results sets as you are doing in another database helps solve the issue of availability with faster response times.

For consistency, you may be able to make use of some form of transaction isolation. For example, MySQL supports a number of different transaction levels, of which REPEATABLE READ may go close to providing you with some consistency in a single transaction. If a transaction can be left open for multiple requests as the users drill down to see the data, they effectively see a snapshot of the database state as of the first request.

In a more generic sense, you're just after a handle which to the data which is provided by the client to indicate a consistent set. As in Patashu's answer, the handle for a client requesting a set of aggregates could be time based. The first stage of client interaction would be to get a handle to the latest aggregate data, eg the current time. If would then pass that handle with each request. As requests are made of the server, it uses the handle to determine which set of aggregate data to return. Rather than having both server "B" and "C", all aggregate data could be stored in server "B", with all aggregate data containing the handle information. This then allows requests to a single server for aggregate data both new and old. At some point, old aggregate data could be purged from "B".

Perhaps a search on transaction isolation will turn up more results for discussion on consistency.

Answer 3

I think you're looking for Data Warehousing concepts

In computing, a data warehouse or enterprise data warehouse (DW, DWH, or EDW) is a database used for reporting and data analysis. It is a central repository of data which is created by integrating data from one or more disparate sources. Data warehouses store current as well as historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons.

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Unlike the ETL-based data warehouse, the integrated source data systems and the data warehouse are all integrated since there is no transformation of dimensional or reference data. This integrated data warehouse architecture supports the drill down from the aggregate data of the data warehouse to the transactional data of the integrated source data systems.