|By Michael Kopp||
|October 27, 2012 10:00 AM EDT||
Traditional Enterprise Database vendors often bring up the lack of professional monitoring and management tool support for NoSQL solutions. Their argument is that enterprise applications require sophisticated tuning and monitoring of the database in order to ensure a performant and smooth operation. NoSQL Vendors, while arguing that this lack is not enough to favor RDBMS over their respective solutions, do agree. Several vendors try to differentiate themselves by providing enterprise level monitoring and management software, for example, Cassandra, MongoDB, HBase or others. Both are of course correct that monitoring and management of especially the performance aspect is important, but at the same time they are making the same mistake that RDBMS vendors have made for the last decade: they ignore the application.
Application Performance Management for Databases
What matters in the end is not the database performance itself, but the performance of the application that uses the database. We have explained the different problem patterns numerous times in this blog (...) so I won't go into them. All of them however have one thing in common: the application logic drives how the database is used and there is only so much that you can tune on the database to cover for mistakes on the application side. So we need to monitor and optimize that usage pattern itself. Application Logic again is driven by input data or in most cases end user interaction, thus we need to understand how end user behavior and end user actions drive the database usage. On the other hand we need to understand the impact of the database on these actions. What's important to understand is that the database can work and perform to the highest standards and still be the main bottleneck as far as the application is concerned - if it is wrongly used or has a bad access pattern. RDBMS and NoSQL Databases have that in common. Therefore the fundamental way I, as a performance engineer, do application performance analysis and management does not change:
First we need to understand if a particular business transaction slows down, has a general performance problem and if this has an impact on the end user:
Do any of the business transactions violate a baseline or have negative end user experience
Next we would isolate the high level cause of the slow down or performance issue. There are many ways of doing this, but it's always some kind of fault domain isolation:
Transaction Flow shows that we spend ~15 % waiting for the Database
This Transaction Flow shows that the Business Backend is calling a Cassandra Database Cluster
This tells us if we spend time waiting on the database. We see that there is not much difference between a regular database and something like an Apache Cassandra!
What's important here is that if the database shows up as the main contributor, this does not mean that the database itself is at fault, it might just be the applications usage of it. Thus I would need to check the usage and access pattern next:
This shows the select statements executed within a particular transaction type
This shows all Cassandra database statements against all participating Cassandra servers executed in a particular transaction
Here I might see that the reason for bad performance is that we execute too many statements per transaction, or that we read too much data. If that is the case I would need to check the application logic itself and potentially need a developer to fix it. The developer would of course want to understand where, why and in which particular transaction the statements are executed.
This shows a single Transaction (PurePath) and the Cassandra Statements executed within it
If however a particular statement is slow we, might very well have a database issue and I would talk to the DBA. The only difference in case of a NoSQL solution in this process is that you often have a database cluster, so I would want to understand if the problem is isolated to a particular node or not. And the DBA will want to understand if my access pattern leads to a good distribution across that cluster or if I am hammering away at a subset of them.
This hotspot view shows that Cassandra Server Node3 consumes much more Wait and I/O time than the others
The nice thing is that all in all my analysis does not differ between JDBC, ADO, Cassandra or one of the many NoSQL solutions.
APM Solution Support
There is of course a caveat here; it requires some level of support of the APM solution of choice. Sometimes it might be enough to see API calls on the NoSQL client in my response time breakdown. More often than not of course a little bit more context is desired, like which ColumnFamily is accessed and how many rows are read or which Database Node in the Cluster is serving a read. For this and the afore mentioned reasons I argue that APM solution support of your chosen Database or NoSQL solution is as important as the monitoring of the database itself.
I have spent considerable time tuning SQL statements and indexes, but in the end the best optimizations have always been those on the application and how the application uses the database. SQL Tuning almost always adds complexity and often is a workaround over bad application or data structure design. In the NoSQL world "SQL statement" tuning for the most part is a task of the past, but Data Structure Design has retained its importance! At the same time logic that traditionally resided in the database is now in the application layer, making application design even more important than before. So while some things have shifted, from an Application Performance Engineering Perspective I have to say: nothing really changed, it's still about the application. Now more than ever!
Just over a week ago I received a long and loud sustained applause for a presentation I delivered at this year’s Cloud Expo in Santa Clara. I was extremely pleased with the turnout and had some very good conversations with many of the attendees. Over the next few days I had many more meaningful conversations and was not only happy with the results but also learned a few new things. Here is everything I learned in those three days distilled into three short points.
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