Hybrid Workloads and HTAP

NuoDB's peer-to-peer architecture allows the database to configure in-memory peers independently of each other. This enables you to optimize your compute resources and meet hybrid workload requirements within a single logical database. In a mixed read/write workload scenario, for instance, NuoDB can support one set of peers focused on transactional processing and another set configured to execute long-lived analytical queries without blocking the ongoing operational workloads.

A perfect example of this is the area of hybrid transaction/analytical processing (HTAP), the ability to perform both online transaction processing and real-time operational intelligence processing simultaneously and within the same database.

The underlying architectures for transactional and analytical databases have traditionally been separate and distinct, with each offering optimized to perform only their own specific and distinct functions. NuoDB’s peer-to-peer architecture overcomes this hurdle by allowing you to dedicate database and hardware resources to perform either near real-time analytics or low-latency, high-throughput transaction processing.

Big data analytics hype has tended to focus on discovery - essentially putting together data lakes and pointing Hadoop at them. However, the reality of many data-driven businesses is their need for real-time optimization. Businesses want repeatable analytics on very recent data. This provides immediately valuable operational intelligence without the time and effort required to offload data to a separate analytics database. Unfortunately, traditional lock-based database management systems are very poor at this.

The unique advantages of NuoDB’s architecture are to provide very low latency SQL analytics on fully up-to-date data. The architecture was further optimized to enable these HTAP capabilities. Advantages inherent in the architecture include:

  • Ability to run transactional and analytical workloads against a single operational data store (ODS) without experiencing performance degradation due to lock contention.
  • Ability to utilize dedicated high spec analytics servers to process long-running SQL queries for improved query performance.
  • Ability to scale out/in analytical workloads on-demand.
  • Ability to access and query the NuoDB database with leading JDBC and ODBC-compliant data visualization and reporting tools.

Single Logical Database Diagram

How It Works

NuoDB is a transactional system, meaning that it’s well suited to support both online transaction processing (OLTP) tasks and operational workloads like account management or session tracking. The operational model is typical of scale-out web applications, which need a database that can support many concurrent clients doing regular, small, localized updates. While techniques like database sharding or replication are hard to apply to OLTP, they can be used for operational workloads that have strong locality. The problem is that these approaches make it hard to do real-time analysis of the data.

NuoDB provides a scale-out architecture, supporting transactions that need to read large quantities of data. Because multi-version concurrency control (MVCC) is used to enforce consistency, those long-running transactions can execute without causing conflict with the many short-running updates happening across the database. In this way, NuoDB supports scale-out operational data deployments where real-time reporting and analytics operations need to be run on the same data set.

Because NuoDB has a flexible load-balancing policy, it’s also possible to dedicate specific transaction-focused peers (Transaction Engines in NuoDB) to specific roles. For instance, a single database can be scaled out across smaller systems for typical operational access patterns. One or a few larger systems (with more memory and processing power) can be dedicated to running analytic transactions. The application is still viewing a single, logical database that is always consistent across all the hosts but with appropriate resources dedicated to specific tasks.