Close-to-the-metal architecture handles millions of OPS with predictable single-digit millisecond latencies.
Learn MoreNoSQL vs SQL Performance Monitoring and Tuning
The ongoing maintenance of a database requires close monitoring and frequent performance tuning. As datasets grow and application traffic increases, administrators need to keep a close eye on disk space, CPU consumption, memory allocation, and index fragmentation.
In general, NoSQL databases have more predictable performance than SQL databases, which can make performance tuning and monitoring an easier lift. NoSQL databases also make it easier for database administrators to monitor and manage database deployments – especially across a hybrid cloud environment.
What Does Database Maintenance and Administrative Overhead Involve?
As development teams become smaller and more agile, they are also increasingly sensitive to database maintenance and administrative overhead. On application-focused teams, database experts are becoming less and less common. Traditional database administration and maintenance responsibilities are often rolled into ‘full-stack’ developer and DevOps
positions. Operational overhead is often in direct competition with product development efforts.
For these reasons, the choice of a database must take into account the expertise of the organization and the need or desire to build up internal expertise around a given technology.
Performance adjustments are proprietary to each database and often require significant dedicated expertise – even between SQL vs NoSQL databases.
How Does Database Overprovisioning Compare in SQL vs NoSQL?
A database administrator never wants to see database utilization spike over 100%. Therefore, administrators must provide a buffer against traffic spikes by ‘overprovisioning’ hardware. The degree to which hardware must be overprovisioned depends on the scaling characteristics of the database. In general, NoSQL databases have a flatter and more predictable performance curve. Therefore, NoSQL databases tend to require administrators to minimize overprovisioning without compromising safety.
What Does Performance Tuning Involve in both SQL and NoSQL Databases?
Performance tuning can be used to minimize overprovisioning, but it can only go so far in preventing full utilization. When performance tuning hits a wall, the database must be scaled; the RDBMS administrator has two choices. First, the dataset can be ‘sharded,’ such that a subset of the data is stored on each node. Second, the administrator can add more powerful hardware, increasing the capacity of hardware by adding more powerful CPUs, more storage, and faster networking components.
Often, teams do both, sharding and scaling, which adds both complexity and cost. The vertical scale adds significant cost at each step and eventually runs up against the physical limits of the network.
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How is NoSQL Database Management Different from SQL Databases?
NoSQL databases make it easier for administrators to monitor and manage database deployments. First, they tend to be capable of running at higher levels of utilization than most RDBMSs. Second, capacity can be increased by adding new nodes running on inexpensive commodity servers. But within the family of NoSQL databases, these two capabilities vary considerably.
Some NoSQL databases also require expert administrators with detailed knowledge of proprietary tuning settings. Others adopt a more automated approach that minimizes tricky manual tuning parameters, enabling non-specialties to conduct NoSQL database management.
Likewise, some NoSQL databases take a horizontal scale to an extreme, often requiring huge clusters to achieve the required performance targets and maintain SLAs. Sometimes these clusters run into the tens of thousands of nodes. While providing a frictionless path to scale, this approach also increases operational overhead. The ideal nonrelational database can efficiently use powerful modern hardware, while also enabling clusters to grow and shrink elastically with minimal administrator intervention.
SQL vs NoSQL Backup and Recovery
In both RDBMS and NoSQL worlds, data can become corrupt due to hardware issues, software bugs, and user errors. The resilient architecture of NoSQL databases typically provides a buffer against data loss. Still, administrators need to be able to restore the data to a known ‘good state.’
A backup and recovery plan is essential, being built around two core targets: Recovery Point Objective (RPO) and Recovery Time Objective (RTO).
- RPO is defined by the age of data in backup storage needed to resume normal operations after a failure.
- RTO defines the time needed to restore the system to a normal state.
A classic database restore plan might include a single daily backup along with differential backups every hour to support a one-hour RPO. For a large database, the recovery time for a full restore can take hours to days, and every backup takes additional storage space.
NoSQL Node Repair and Replacement
Given the distributed nature of NoSQL clusters, nodes occasionally fall out-of-sync. To address this issue, NoSQL databases provide tools to bring out-of-sync nodes up-to-date using a repair procedure.
Repairs populate the node to match the data on the other replicas. Sometimes a node can fall so far out-of-sync with the cluster that it needs to be replaced. As they are bootstrapped into the cluster, fresh nodes must stream a copy of the whole dataset; for large datasets, such a refresh can take an inordinate amount of time. NoSQL databases perform such operations using a variety of algorithms, some of which are more efficient than others. Thus, some NoSQL databases recover more quickly and predictably than others.
