If you find that your application is facing load problems, it means that you have reached the maximum capacity of users that your application can handle. Ultimately, things may start to slow down and fail. You can find the early symptoms as the network requests getting timed out, database queries taking a lot of time to execute, pages loading slowly, or not. The immediate need is to scale your application. It is vital that you have to address this growing pain before your users start to leave your app by getting annoyed.
Cost of scaling
Once you find the database is exhausted and before you start to sharding it horizontally, vertically, or inside out, there is an important thing to consider. You should not implement any premature optimization to scale your application up before understanding the actual requirement. Any attempt to implement the scaling solutions may face the below complexities:
- Adding more features may take more time.
- The system may become more complex as more variables getting involved.
- Codes may become tougher to test.
- Spotting and resolving the bugs in the apps may become stricter.
So, you should take these trade-offs only when your app reaches its capacity. Try to keep the entire system responsive and straightforward, and do not introduce any scaling complexities until it is entirely warranted. Inside-out sharding is not a wise solution at all.
Finding constraints using metrics
Each application and system functions in a unique way. So, to determine the scaling approach to consider, you must first identify where the bottleneck is. Thus, the primary need is to check for your existing resource monitoring system and create one. Whatever the stack of monitoring application you use, there could be some tools available to monitor the resources. If you are using any of the IaaS (Infrastructure as a Service) software like Microsoft Azure, AWS, and GCP, you may find plenty of tools to utilize.
All these tools will help to easily interpret your resource performance with easy graphics and other visualization techniques. You may look at these graphs to check for the flat tops and spikes to identify your resources’ behavior. With this, you can quickly determine whether your resources are overwhelmed or reached to the upper limit of its capacity. If it has not reached the capacity, but still you find your application running slow, you may also try to sprinkle the logs throughout the most-used operations.
Scaling your apps
Once you get a good understanding of where your bottlenecks are, you may start exploring the solutions to resolve these. In any case, simplicity should be considered as the key and always avoid bringing in any unwanted complexities.
The primary objective of a database scaling solution is to relieve the stack from its workload based on the most common requests of the applications. This has to be achieved by distributing the workload effectively across various resources how the most common scaling techniques may ideally translate into any of the following cases as per RemoteDBA.
- Reusing data that the app already is done with.
- Eliminating the requests from users which the app possesses already.
- Storing common operations results in reducing any repeated computational needs.
- Avoiding any complex operations in the standard request-response approach.
Most of the scaling approaches may further boil down into some or other form of caching. In the past, memory space was very costly and also with limited availability. However, now it has become much inexpensive and widely accessible.
Solutions for Database Scaling
As we had seen above, caching the database queries is the simplest and most effective improvement you can do to handle the increasing database load. Applications may have a handful of queries that make up the majority of the queries made. So, rather than making the whole round trip over the network each time, the question can be easily cached in the web server’s memory. The primary request will fetch the data from the database and then cache its results on the server, and the same requests when come further will reach from this cache. This approach will result in increased performance as the round trip is cut short, and the information is shared from the closest location to the user.
This approach will also help get most of the database server resources made available locally, to which a significant frequent workload is getting distributed. Even when the database is not available, this cache can offer ongoing service to the application to make the whole system more resilient to any failures. There are many tools you can use to analyze the database query logs to see the queries which take the longest route to complete and which queries are coming more frequently from the users.
Indexing is another technique that can help to speed up data retrieval from the database tables. Indexes can help locate the data quickly without searching for each row in the tables when some are trying to access the table. Usually, these data structures for a particular database may act like binary search trees.
So, based on the number rows in the database table, doing proper indexing can save a lot of time spent on queries using the indexed columns as references. For example, if you have ten thousand users and the application has a profile page that needs to look up for the users by their user name, then a random query may need to examine every row in the table to find the particular match. This is about 10,000-row examinations to take place. However, by creating a ‘username’ index, the database can pull out that specific row under logarithmic time complexity. The maximum number of examinations to be held will be reduced to just 14 from 10,000.
Other quick modes of enhancing database scalability include the options of session Storage, Master-Slave Replication, Database Sharding (horizontal and vertical), etc. Remember that an effectively scaled database server is essential to ensure your enterprise applications’ performance and upkeep.