Amazon Aurora Monitoring Tool

Amazon Aurora is a fully managed relational database engine available as part of the Amazon Relational Database Service (RDS) from Amazon Web Services (AWS). Amazon RDS is a managed database service that simplifies the process of setting up, operating, and scaling a relational database in the cloud. It takes stress out of the many database management tasks that would otherwise take up a huge amount of time for an IT admin.

Aurora offers the simplicity and cost-efficiency of an open-source database while also providing the high-quality security, reliability, and availability you can expect from an enterprise-grade database. In fact, according to Amazon, Aurora can deliver as much as three times the throughput of PostgreSQL and up to five times the throughput of MySQL. Its database engines are customized to leverage rapid distributed storage to ensure fast response times. As with any database solution, it requires constant Aurora cloud monitoring to ensure optimal performance.

Amazon Aurora is a pay-as-you-go service and doesn’t come with any upfront fees. It has two different pricing structures depending on whether you use the MySQL-compatible version or the PostgreSQL-compatible version. Within those categories, you can choose if you want to invest in the standard instances option or the memory-optimized instances option. Aurora is highly scalable, making it a viable option for organizations that may grow in the future.

Amazon Aurora monitoring is critical for maintaining the performance and reliability of your database engine. Aurora cloud monitoring consists of continuously collecting and analyzing data from the database engine, so you can better identify and resolve problems as they arise.

When it comes to Aurora monitoring, there are several key categories of metrics you need to track. When any of these metrics fall outside their expected values, they can indicate a problem within the system that could lead to a deterioration in performance and reliability. The more closely you monitor the metrics, the faster you’ll be able to identify a potential problem, and the faster you’ll be able to address it.

The metric categories to keep an eye on:

Query throughput: Check to ensure your queries are being executed. Use Amazon CloudWatch Aurora to monitor the breakdown of read and write commands and identify potential bottlenecks.

Query performance: This includes metrics like latency—a critical database data point. Any instances of latency can lead to serious slowdowns and bottlenecks if they aren’t addressed as soon as they arise.

Resource utilization: Aurora needs four fundamental resources: memory, CPU, network, and disk. Any one of these can become a performance bottleneck if it isn’t being utilized properly. These metrics are usually the first place to turn if any database instances start having performance problems.

Connection: Aurora has a configurable connection limit, but you always want to make sure your max connections aren’t too low or too high.

CloudWatch Aurora metrics: You can obtain several metrics through Amazon CloudWatch. These include metrics related to RAM to help you ensure your working set resides almost completely in memory. When it doesn’t, your speeds are exponentially reduced, so you want to keep a close eye on metrics like BufferCacheHitRatio and BufferCacheHitRatio to make sure you have enough RAM allocated to Aurora.Tracking all these metrics manually can be a challenge. That’s why effective Aurora monitoring requires investment in a quality Aurora cloud monitoring tool designed to track all these metrics for you, aggregating and analyzing them, so you can easily see where the values indicate a potential problem.

Aurora is a cloud-based custom database engine for Amazon RDS. As such, its features help it stand out from other database engines to bring you a faster and more modern database experience. As with any database, Aurora stores data in tables accessible through the creation of queries. Aurora is compatible with both MySQL and PostgreSQL query languages.

Aurora uses the Amazon RDS AWS Management Console interface, as well as API operations and AWS CLI commands to handle its routine database tasks, including patching, provisioning, recovery, backup, failure detection, and repair. The database engine’s management operations typically involve whole clusters of database servers, instead of using individual database instances. These clusters are synchronized through replication. Automatic clustering, replication, and storage allocation are just a few of the assets that make Aurora a simple and cost-effective solution.

In addition to all this, Aurora has features that elevate it above other Amazon RDS solutions. The database engine has a fault-tolerant, self-healing storage system, high throughput, and storage auto-scaling. It also offers point-in-time recovery and continuous backup with replication across three separate availability zones to keep your data secure. In addition to the auto-scaling, Aurora provides rapid automated failover to a standby instance and extremely low-latency replication, allowing it to offer throughput enhancements up to five times that of MySQL and three times that of PostgreSQL running on similar hardware.

Aurora really stands out when it comes to Aurora monitoring. When you use Aurora, you get access to an expanded suite of monitoring metrics compared to those available to other RDS users. It exposes crucial metrics tied to latency and query throughput in addition to the more standard system and disk-level metrics.

SolarWinds Database Observability is an Aurora monitoring tool designed to help you gain insight into key metrics and queries you need to optimize your Aurora database engine performance.

We offer 24/7 monitoring of your Aurora database engine with both real-time and historical analysis. The Aurora monitoring tool is built to bring you one-second granularity into tens of thousands of metrics it’s constantly collecting from across your database engine. This granularity can help you identify, diagnose, and address the smallest performance problems before they grow into major bottlenecks or even outages.

One of the biggest assets of SolarWinds Database Observability is the way it makes the data it collects easily understandable and consumable. One of the ways it does this is by offering both daily and weekly summary reports covering key findings. Another way is through its intuitive, comprehensive dashboards and other visualizations. In addition to a variety of preset dashboards comparing different metrics, it’s easy to create customized graphs, charts, and full dashboards, so you can visualize collected metrics in whatever configuration makes the most sense for you.

Features like the Top Queries page enable you to move beyond simply rectifying problems to proactively optimizing your queries and your database engine to improve performance. On these pages you can see recommendations for improvements based on insights drawn from the tool’s analysis of Aurora metrics and queries.