When to Use a NoSQL Database Performance: Large Table Scaling and Customization

When to Use a NoSQL Database Performance: Large Table Scaling and Customization

For most small businesses, a NoSQL database is more than enough to hold all their data. However, if you have a large database with a lot of columns, you might have some problems if you’re not careful. This blog will help you understand when you should be using a traditional relational database and when it’s better to use a NoSQL database performance. You’ll also learn how to scale a relational database and how to customize it to fit your needs.

As needs change, so do customer expectations. In all industries, clients want you to treat them as individuals and understand and meet their demands. Personalization means you give a customized experience to each customer based on their needs and preferences; you set up a process to create individualized interactions that improve the client’s experience. 84% of consumers feel being treated like a person, not a number, is key to winning their business.

Whole sectors are undergoing digital change to better offer customers individualized experiences. Retailers boost engagement and conversion with personalized content, offers, and product suggestions. Using customer information like interests, purchasing intent, and buying activity, ad tech companies increase the relevance and efficacy of their ads. Digital music services let clients find new music, playlists, and podcasts based on their listening habits and preferences.

As clients want greater personalization, contemporary technology helps more businesses provide it. We’ll look at some typical problems with adopting personalization and how to tackle them with disruptive database technologies like Google Cloud’s Bitable. Big table’s petabyte size, high availability, high throughput, and price-performance benefits let you personalize at scale.

Customization issues

Data fuels personalization. To enable customization at scale, an application must store, manage, and access enormous volumes of data (user-specific and anonymized aggregate data) to understand the behavior, needs, and preferences of each consumer. Your database must write massive volumes of data for all active clients promptly. Continuously capture customer behavior data since each step can inform the next. For example, adding an item to a shopping cart can trigger recommendations for related or complimentary products. Much of the personalization data is semi-structured and sparse, requiring a flexible database.

Personalization at scale requires massive volumes of data to be read in near real-time to be in the essential serving path to create a smooth user experience, sometimes with less than 100ms of application latency. NoSQL Database Performance requests must provide results in single-digit milliseconds. Make sure application latencies don’t worsen as you add customers. Data must be organized properly and connected with other tools to conduct deep analytical queries, apply machine learning (ML) models to produce personalized suggestions, and store the aggregates in your operational database for serving clients. You must be able to do huge batch reads for analytics without slowing down your app.

Make sure your database prices don’t skyrocket as your program becomes more popular. Your database must achieve low TCO and good price performance as data volumes and throughput need to expand. Your database must scale effortlessly and linearly to give a predictable performance to global users. Also, it should be easy to manage your database so that you can focus on your application.

Why NoSQL Database Performance is Good for Personalization

Each database has engineering tradeoffs. 40 years ago, storage, processing, and memory were thousands of times more expensive than today. Databases were deployed on a single server to a modest number of concurrent users who accessed the services during typical business hours. Relational databases were created with this in mind. They strive to be storage and memory efficient and presume a single server.

As the cost of storage, memory, and processing reduced and data and workloads rose, engineers began to rethink these choices with new goals. Later, new types of databases assume distributed designs to be easier to grow, especially with cloud infrastructure. With this strategy, you give up SQL and many of the data integrity and transactional capabilities of relational systems. These are NoSQL database performances.

Relational databases presume a set schema that rarely changes. The predictability of data structures provides for many optimizations but makes it tough to add new and varied data items to your program. NoSQL database performances like key-value stores and document databases relax the schema and allow data structures to develop over time. Flexible data models speed application innovation and iteration on ML models, which is key for personalization. Cloud Bigtable’s ability to grow lets you customize experiences for millions of users at the same time as you grow your business.

How Bigtable Scales Personalisation

• Cloud Bigtable can handle millions of queries per second, store petabytes of data, and achieve single-digit millisecond read and write latencies. Bigtable reduces TCO with high performance and low running costs
• Let’s say your app rockets to 250 million users. Let’s say your application has 1.75 million concurrent users, each sending two database requests per minute. This will send 3.5 million queries every minute (58.3K per second) to your database. Pricing for Bigtable to run this workload starts under $400 per day
• Bigtable’s throughput scales linearly
• With compute and storage separated, Bigtable automatically configures throughput by associating nodes and data for consistent performance. When a node is overloaded, Bigtable sends some traffic to a node with less load to enhance performance. BigTable allows cross-region replication with local writes in each region. This lets you manage your data close to where your clients are, which cuts down on network latency and gives your clients predictable reads and writes with low latency
• Bigtable is a NoSQL database performance by Google Cloud. Bigtable has a column family data architecture that lets you store different pieces of data for each customer based on how they act and what they like. You can save a large number of these pieces of data for each customer and easily change your application
• The Bigtable database holds trillions of rows and millions of columns. Each row in Bigtable supports up to 256 MB, so you may store all customer data in a single row. Bigtable tables are sparse; you only pay for the columns that store data
• BigQuery ML lets you design and runs ML models in BigQuery to provide Bigtable personalization recommendations
• You can quickly stream Bigtable data into BigQuery to execute analytical queries and make suggestions. These aggregates, like computed recommendations, are stored in Bigtable so that your app can use them quickly and at a large scale with little latency
• Bigtable interfaces with Apache Beam and Dataflow to help you examine data
• With application profiles and replication in Bigtable, you can segregate your workloads so batch reads don’t slow down a mix of reads and write. Your app can conduct near real-time reads at scale to construct and train TensorFlow machine learning models for personalization. Bigtable gives you the right operational data platform to make personalized suggestions offline or in real time

Advantages of NoSQL Database Performance

There are various benefits to adopting NoSQL database performances, including:

• NoSQL database performance facilitates application development, particularly for interactive real-time web applications leveraging a REST API and web services
• These databases give flexibility for data that hasn’t been normalized, which requires a flexible data model or has distinct features for different data entities
• They offer scalability for bigger data volumes, which are common in analytics and AI applications
• NoSQL database performance is more suited for the cloud, mobile, social media, and large data
• They’re created for certain use cases and are easier to use than relational or SQL databases for those applications

NoSQL Database Performance Disadvantages

The downsides of a NoSQL database include:

• Every NoSQL database has its own syntax for querying and managing data. This is in contrast to SQL, the lingua franca for relational and SQL database systems
• The lack of a formal database structure and constraints disables data integrity safeguards built into relational and SQL database systems
• A schema with structure is needed to use the data. In NoSQL, this is done by the application developer, not the database administrator
• Because most NoSQL database Performances use the eventual consistency paradigm, they do not provide the same level of data consistency as SQL databases. Sometimes the data is inconsistent, which makes them unsuitable for operations that demand immediate integrity, such as banking and ATM transactions
• Because NoSQL databases are newer, there are no broad industry standards as with relational and SQL DBMS products

NoSQL vs. SQL: What’s the difference?

• SQL databases are for general purposes, but NoSQL databases are intended for specialized use cases. The key differences between NoSQL and SQL are API, data model, schema need, scalability, and data integrity. Each uses a distinct technique for data storage and retrieval
• API. For NoSQL, SQL is not necessary as an API to the database data, however many offer a SQL-like query language. SQL is often the main or primary interface for SQL databases
• Data model. With NoSQL database systems, data is not treated as tables with fixed rows and columns, as with SQL DBMS. Instead, data might be modeled as JSON documents, graphs with nodes and edges, or key-value pairs. Wide-column storage uses the table and row structure, but columns can be dynamic from row to row within a table
• Schema. The schema for a NoSQL database is variable, therefore data types and lengths are not defined. Data can be stored in a free-form, schemaless fashion. This strategy allows programmers more freedom, which can ease development processes
• With SQL databases, the schema is fixed, with rigid data types and lengths for each column, and every row must follow the defined column layout and structure. If a column is defined as an integer, only integer data can be placed in it, and any effort to do otherwise is refused by the DBMS. This technique improves data quality since the DBMS enforces rules as data is added
• Scalability. NoSQL database performance usually implements horizontal scalability or scaling out. Scaling out means adding extra hardware to a system, mainly commodity servers. NoSQL systems employ horizontal partitioning with sharding to break up huge databases into smaller bits over numerous servers
• The SQL strategy is vertical scaling or scaling up. Vertical scaling adds resources, such as a more powerful CPU or more memory, to manage greater workloads or improve performance
• Data integrity. NoSQL and SQL database performance utilize distinct ways to secure the integrity of data when it is created, read, modified, and removed
• Most NoSQL database systems maintain data integrity via BASE (Basically Available, Soft State with Eventual Consistency). Using BASE, data may be inconsistent for a while, but database replication ultimately updates all copies to be consistent. Some programs can handle inconsistent data, whereas others can’t
• SQL databases employ the ACID method. Each of its four attributes — atomicity, consistency, isolation, and durability — contributes to a transaction’s data integrity. Using ACID, each transaction — when conducted alone in a consistent database state — will either complete with proper results or terminate with no effect. In either instance, the database state will be consistent

Conclusion

Today, we’re going to explore when it makes sense to use NoSQL. As you know, NoSQL is a broad category of database technologies. There are many different types of NoSQL databases, and each has its own benefits and limitations. Today, we’re going to focus on one type of NoSQL database called Bigtable. We’ll explain how Bigtable is similar to a table in a relational database but doesn’t have the same limitations.

About Enteros

​Enteros offers a patented database performance management SaaS platform. It proactively identifies root causes of complex business-impacting database scalability and performance issues across a growing number of RDBMS, NoSQL, and machine learning database platforms.​