SolarWinds Observability SaaS and NoSQL Databases

ewen 5 months ago 5 minute read time

Recent numbers estimate that approximately 400 million terabytes of data are created each day. This explosion of data has led to a complete reconceptualization of how best to store it. Traditional SQL databases aren’t always able to handle the many new scenarios that interactive web applications introduce. The huge volume and variety of data, along with the need to query rapidly and efficiently, has led to the development of NoSQL databases.

NoSQL databases are flexible, non-relational systems designed to manage different data types without mandating a rigid schema. However, organizations that have adopted NoSQL databases have encountered unique challenges that require strong monitoring and troubleshooting tools.

In this article, we’ll explore the different types of NoSQL databases and the challenges they pose. We’ll also demonstrate how SolarWinds^® Observability^® SaaS provides key insights that help engineers effectively solve performance and availability issues.

NoSQL Databases: A Modern Solution

Modern enterprise-level web applications handle hundreds of millions of user requests every day, generating oceans of data along the way. NoSQL databases often outperform their SQL counterparts in these high-traffic environments, especially in write-heavy applications. Unlike relational databases, they don’t mandate a strict schema, which allows for far greater flexibility when storing data.

Developers typically design NoSQL databases to scale horizontally by partitioning data across a distributed set of nodes. This approach enables them to manage increasing loads more efficiently as traffic grows.

Some of the most commonly used NoSQL database types include the following:

Key-value stores: These stores retrieve data with a simple key-based query and perform exceptionally well in caching or session management scenarios. However, they are not optimal solutions for use cases that require complex queries. Examples are Redis and Memcached.
Document stores: These store data in JSON, XML, or BSON formats, maintaining high performance while supporting complex queries and ACID transactions. JSON formats are generally considered intuitive, as they are human-readable and map easily to objects in programming languages. Some examples are MongoDB and CouchDB.
Time-series databases: These track data through time, such as observability metrics or stock market data. Examples are Prometheus and Timescale DB.
Graph databases: These databases are built on the mathematical graph theory principle and store data as nodes and edges. This is ideal for showing relationships between data points, as used in recommendation engines, social networks, and fraud detection. Examples include Neo4j and ArangoDB.

The Challenges of NoSQL

Although NoSQL databases have evolved over time, many challenges remain.

Complex queries

Executing complex queries can often overwhelm hardware resources, leading to high latency levels and a diminishing user experience. Queries that require sophisticated data aggregations are common causes of bottlenecks.

Optimizing for scale

Although NoSQL databases generally excel at scaling, optimizing scaling levels is often challenging. Excessive scaling is costly and can significantly increase your organization's cloud expenses if not correctly configured. Insufficient scaling can negatively impact performance, resulting in poor user experience.

Meeting data consistency requirements

For certain types of applications, such as banking or travel reservations, strong data consistency is imperative because, eventually, inconsistent data can lead to a lack of synchronization that may result in financial losses, double bookings, or other critical failures. Choosing the right database to achieve strong consistency in a distributed environment is a well-known challenge as it has inherent performance tradeoffs.

Resolving these challenges becomes significantly easier with tools that provide extensive observability. A robust observability solution can simplify the tasks of identifying and troubleshooting the root causes of the issues.

Along with the significant flexibility offered by NoSQL databases, tools like SolarWinds Observability SaaS are essential for ensuring peak performance.

Monitoring NoSQL Databases With SolarWinds Observability SaaS

SolarWinds Observability SaaS provides database engineers with valuable insights into the NoSQL databases running in their environments. In this section, we’ll demonstrate several main features with an example of the MongoDB database, which stores data in binary JSON format (BSON).

Initial connection and setup

For SolarWinds Observability SaaS to monitor and capture data from MongoDB, you must connect the MongoDB server with SolarWinds Observability. View this detailed guide for more information.

After successfully connecting the MongoDB instance to SolarWinds Observability SaaS, analyze observability data by logging into the UI console of the SolarWinds Observability account.

View Key MongoDB Database Metrics

For a quick view of key performance metrics—such as throughput, response times, and error rate—begin by navigating to the Databases page in SolarWinds Observability SaaS.

Next, click on View All Databases in the upper right-hand corner.

You’ll see a list of all the databases connected with SolarWinds Observability SaaS. Find your database instance, which will be database type MongoDB, and click on it.

The “database details” screen will display the overview of the MongoDB database with metrics, such as overall error rate, response times, throughput, and entity health score:

View Performance Metrics for Specific Queries

To view the performance metrics of each query, click on the Queries tab:

To inspect a specific query execution, navigate to the Samples tab, which will display a list of executed queries:

In the All Samples table at the bottom, select a particular query to examine it more closely.

You can troubleshoot performance issues by clicking Explain Plan in the side menu that slides in from the right.

This feature analyzes slow queries and identifies areas for optimization, saving time and improving database performance.

View Hardware Performance Metrics

To view the efficiency of the MongoDB hardware serving up your database, click the Resources tab.

This view will give you insight into golden metrics such as CPU, memory, disk storage, and network.

View Individual MongoDB Metrics

You can also view individual system metrics related to your MongoDB instance. Navigate to the Metrics Explorer page.

Then, filter the metrics displayed by selecting Database Instances and selecting your MongoDB instance among the entity types.

You can also look among all entities and filter down to metrics that begin with dbo.mongo.

Click on any specific metric to analyze it in more detail. For example, view the current number of connections over time:

Conclusion

NoSQL databases continue to garner wide adoption, especially as the variety and veracity of application data continue to grow. These databases solve specific problems and offer great flexibility and scalability, but they can also introduce complexities. To troubleshoot these performance observability challenges, tools like SolarWinds Observability SaaS provide real-time insights and critical metrics to ensure peak performance.

SolarWinds Observability SaaS is a feature-rich, fully optimized observability solution that integrates with different types of NoSQL databases, including MongoDB, to provide indispensable metrics. This allows engineers to efficiently manage and optimize NoSQL databases and quickly resolve any issues.

Ready to take control of your NoSQL databases? Start a free 30-day SolarWinds Observability trial and unlock critical insights to optimize performance and scale with ease.