How to scale a database

Scaling your database is a great way to deal with performance bottlenecks, but you need to understand the limitations in order to determine which solution provides the greatest benefit for you. In 2007, the first NewSQL system, H-Store, was developed. NewSQL systems attempt to combine NoSQL scalability with ACID transactions and SQL interfaces. Your business is growing in America, South Asia & in few countries in Europe. You are doing millions of bookings daily with billions of request hitting your server.

This, obviously, increases the complexity of the whole environment. Horizontal Scaling, as the image depicts is scaling of the server horizontally by adding more machines. Suppose you have a database server with 10GB memory and it has exhausted. Now, to handle more data, you buy an expensive server with memory of 2TB. If we do need the data occasionally and we don’t need any UNIQUE indexes across the whole table, we can simply divide the data into multiple tables. For instance, we can separate data from different customers or store older data in archive tables. This way we can keep our tables smaller and more performant.

Scaling Prerequisites

Scaling in DBMS is the ability to expand the capacity of a database system in order to support larger amounts or requests and/or store more data without sacrificing performance. MongoDB has the ability to store both sharded and unsharded collections in a sharded cluster. This allows the application to take full advantage of the cluster for large data sets while using a primary shard for small data sets. Replication refers to creating copies of a database or database node. If one of the nodes goes down, the cluster is still able to serve client requests because the other nodes in the cluster can respond to the requests. The CPU and/or memory becomes overloaded, and the database server either cannot respond to all the request throughput or do so in a reasonable amount of time. The main problem that you have to deal with is that adding more and more nodes makes it hard to manage the whole environment.

• You now take two more big machines & set them up as replica to the current machine.
• Fully exploiting a hardware configuration requires a variety of locking techniques, ranging from locking an entire database to entire tables to disk blocks to individual table rows.
• Fauna is a serverless cloud database that dynamically adjusts capacity so that you never run out of storage and pay for only what you use.
• For example, in OLTP systems, many transactions may attempt to insert data into the same table at the same time.
• Scaling in DBMS is the ability to expand the capacity of a database system in order to support larger amounts or requests and/or store more data without sacrificing performance.
• This allows the application to take full advantage of the cluster for large data sets while using a primary shard for small data sets.
• The cache sits in-line with the database and writes always go through the cache to the main database.

When using asynchronous replication, if the master fails then the data will not be available on the slaves. The only option to scale writes requests is to scale up the Master node. As your application scale, the database will often be the main bottleneck of the system.

Pattern 6 – Horizontal Scaling:

Base One makes the case for extreme scalability within mainstream relational database technology. You now take two more big machines & set them up as replica to the current machine. Database replication will take care of distributing data from primary machine to replica machines. You navigate all read queries (Query in CQRS) to the replicas — any replica can serve any read request, you navigate all write queries (Command in CQRS) to the primary. There might be little lag in the replication, but according to your business use case that’s fine.

For the load to be distributed evenly, you will probably need to put in a load balancer. Additionally, like many other distributed systems, additional software like Apache Zookeeper is needed to handle resiliency at scale and synchronization across servers. Managing and running Zookeeper can take up valuable IT and engineering resources if you don’t have the right expertise. Some researchers question the inherent limitations of relational database management systems. GigaSpaces, for example, contends that space-based architecture is required to achieve performance and scalability.

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Another example case where scaling is required is a sudden increase in the workload. For some reason your infrastructure experiences a significant increase in the load on the database cluster. CPU load goes over the roof, disk I/O is slowing down the queries etc.

A basic technique is to split large tables into multiple partitions based on ranges of values in a key field. For example, the data for each year could be held on a separate disk drive or on a separate computer.

Sharding

Data synchronization can be triggered by schedule (once per hour/day/week). Most database products will not scale in this way, and depending on how this is implemented, applications will need to be re-written to work with the database. Optimizing INSERT Statements – To optimize insert speed, combine many small operations into a single large operation. Send the data for many new rows at once, and delay all index updates and consistency checking until the very end. Optimize data access Find out whether your application is retrieving more data than you need.

A machine’s vertical scaling is limited by its hardware resources. Beyond this limit, you typically need to take the machine offline to install any new hardware.

Leverage the execution plan for query optimization

While this is generally useful, we might want to reconsider it for some of our data when it comes to scaling. Document – Document stores allow for the storage of grouped information with any number of fields that may contain simple or complex values.