This post is cowritten by Arjan Hammink from Infor.
Robust storage and search capabilities are critical components of Infor’s enterprise business cloud software. Infor’s Intelligent Open Network (ION) OneView platform provides real-time reporting, dashboards, and data visualization to help customers access and analyze information across their organization. To enhance the search functionality within ION OneView, Infor used Amazon OpenSearch Service to improve their software products and offer better service to their customers by providing real-time visibility. By modernizing their use of OpenSearch Service, Infor has been able to deliver a 94% improvement in search performance for customers, along with a 50% reduction in storage costs.
In this post, we’ll explore Infor’s journey to modernize its search capabilities, the key benefits they achieved, and the technologies that powered this transformation. We’ll also discuss how Infor’s customers are now able to more effectively search through business messages, documents, and other critical data within the ION OneView platform.
Where Infor started
Infor’s ION OneView was built on top of Elasticsearch v5.x on Amazon OpenSearch Service, hosted across eight AWS Regions. This architecture enabled users to track business documents from a consolidated view, search using various criteria, and correlate messages while viewing content based on user roles. Over time, Infor expanded its functionality to include “Enrich” and “Archive” capabilities, which added significant complexity. The Enrich process would build searchable messages by aggregating related events, requiring constant document updates to the OpenSearch indices. The Archive process would then move these messages and events to Amazon Simple Storage Service (Amazon S3), while using a delete_by_query to remove the corresponding documents from OpenSearch Service. These read-update-write-delete workloads, coupled with large all-encompassing indices with shard sizes of over 100GB, resulted in high volumes of deleted documents and exponential data growth that the system struggled to keep up with. To address increasing performance needs, Infor continually horizontally scaled out their OpenSearch Service domain.
Challenges
The key challenges Infor faced underscored the need for a more scalable, resilient, and cost-effective search capability that could seamlessly integrate with their cloud environment. These included the inability to effectively archive data because of high ingestion rates, resulting in longer upgrade and recovery times. Escalating costs from scaling the solution and the need for custom development to enable newer OpenSearch Service features created significant operational burdens. Additionally, Infor was seeing increasing search latency, with CPU utilization peaking at 75% and occasionally spiking above 90% (as shown in the following figures), demonstrating the performance limitations of Infor’s existing infrastructure. Collectively, these issues drove Infor’s need for a modernized search solution.
SearchLatency Pre-Modernization
CPUUtilization Pre-Modernization
Infor’s journey to modernize search with OpenSearch Service
To address the growing challenges with ION OneView, Infor partnered with AWS to undertake a comprehensive modernization effort. This involved optimizing operational processes, storage configurations, and instance selections, while also upgrading to the later versions within OpenSearch Service.
Operational review and enhancements
As a collaborative effort between Infor and AWS, a comprehensive operational review of Infor’s OpenSearch Service cluster was undertaken. With the help of slow logs and adjusting the logging thresholds, the review was able to identify long-running queries and the archival process consuming the largest amount of CPU capacity. Infor rewrote the long-running queries that used high cardinality fields, reducing the average query time.
Next, the team turned their attention to redesigning Infor’s archival process to reduce stress on the CPU. Instead of a single large index, we implemented independent indices based on customer license types. This improved delete performance by allowing the team to target old indices, using index aliases to manage the transition. We also replaced the delete_by_query approach where a query is sent to locate documents prior to a delete with a standard delete passing document IDs directly, because all the document IDs to be archived were known ahead of time. This reduced round-trip time and CPU stress compared to the sequential search requests performed by delete_by_query. This was followed by the tuning of the refresh interval based on the workload requirements, improving the indexing performance, and memory and CPU utilization.
Storage optimization
The team switched from GP2 to GP3 storage, provisioning additional input/output operations per second (IOPS) and throughput only when needed. This resulted in a 9% reduction in storage costs for most of Infor’s workloads. In all use cases where IOPS was a bottleneck, the team was able to provision additional IOPS and throughput independent of the volume size using GP3, further reducing Infor’s overall storage costs. Additionally, we implemented a shard size-based rollover strategy that provided a sharding strategy where total shards were divisible by the number of nodes to reduce the shard size to the recommended number of less than 50 GiB. This helped ensure an even distribution of data and workloads across the nodes for each index, and the performance improvements indicated that more vCPU would be beneficial given the thread pool queues and latencies. Appropriate master and data node instance types were chosen based on the new storage requirements. To support the reindexing process, the team also temporarily scaled up the storage and compute resources.
Upgrading OpenSearch Service
After optimizing the storage and compute configurations based on best practices, the Infor ION team turned their attention to using the latest features of OpenSearch Service. With the shards now at an appropriate boundary and the memory and CPU utilization at the right levels, the team was able to seamlessly upgrade from Elasticsearch version 5.x to 6.x and then to 7.x in OpenSearch Service. Each major version upgrade required careful testing and client-side code changes to make sure that the appropriate compatible client libraries were used, and the team took the necessary time after each upgrade to thoroughly validate the system and provide a smooth transition for Infor’s customers. This commitment to a methodical upgrade process allowed Infor to take advantage of the latest OpenSearch Service features, such as Graviton support, performance improvements, bug fixes, and security posture improvements, while minimizing disruption to their users.
Optimizing instance selection for performance
In collaboration with the AWS team, Infor carefully evaluated local non-volatile memory express (NVMe)-backed instance types for their ION OneView search cluster, comparing options such as i3 and R6gd instances to balance memory, latency, and storage requirements. For write-heavy workloads, the team found that using NVMe storage provided better performance and price compared to Amazon Elastic Block Store (Amazon EBS) volumes because of the high IOPS requirement of the workload, allowing them to be less reliant on off-heap memory usage. By selecting the most appropriate instance types, the ION OneView search cluster was able to resize and scale down the number of data nodes by 63% while still achieving improved throughput and reduced latency. Staying on the latest AWS instance families was also a key consideration, and the team further optimized costs by purchasing Reserved Instances after establishing a good baseline for their performance and compute consumption, with discounts ranging from 30% to 50% depending on the commitment term.
Results
The following figures show the improvements of the modernization.
New indices with the correct shard size can be seen in the increase in shards, shown in the following figure.
The updated shard strategy combined with a version upgrade led to a ten-fold increase in the volume of traffic and efficient archiving as shown in the following figure.
The SearchRate increase is shown in the following figure.
The following figure shows that the CPU increase was minimal compared to the traffic increase.
The SearchLatency reduction post upgrade and implementation of the new indexing and shard strategy is shown in the following figure.
The following figure shows the monthly spend over the past 4 quarters for two Infor ION products.
Conclusion
Through their careful modernization of the OpenSearch Service infrastructure, Infor was able to achieve 50% reduction in infrastructure costs coupled with a 94% improvement in cluster performance. The optimized clusters are now healthier and more resilient, enabling faster blue/green deployments to process even greater data volumes.
This successful transformation was driven by Infor’s close collaboration with the AWS team, using deep technical expertise and best practices to accelerate the optimization process and unlock the full potential of OpenSearch Service. Infor’s OpenSearch Service modernization has empowered the company to provide an improved, high-performing search experience for their customers at a significantly lower cost, positioning their ION OneView platform for continued growth and success.
Every workload is unique, with its own distinct characteristics. While the best practices outlined in the Amazon OpenSearch Service developer guide serve as a valuable guide, the most important step is to deploy, test, and continuously tune your own domains to find the optimal configuration, stability, and cost for your specific needs.
About the Authors
Allan Pienaar is an OpenSearch SME and Customer Success Engineer at AWS. He works closely with enterprise customers in ensuring operational excellence, maintaining production stability and optimizing cost using the Amazon OpenSearch Service.
Gokul Sarangaraju is a Senior Solutions Architect at AWS. He helps customers adopt AWS services and provides guidance in AWS cost and usage optimization. His areas of expertise include building scalable and cost-effective data analytics solutions using AWS services and tools.
Arjan Hammink is a Senior Director of Software Development at Infor, bringing over 25 years of expertise in software development and team management. He currently oversees Infor ION, a project he has been integral to since its inception in 2010 when he began as a Software Engineer. Infor ION is a robust middleware designed to streamline software integration, a key component of Infor OS, Infor’s cloud technology platform.
