March 2019 Summaries

The text discusses the use of frozen indices in Elasticsearch, particularly within hot-warm architecture, to optimize resource usage and cost efficiency when managing large datasets, such as logs and metrics. It explains how frozen indices maintain searchability while significantly reducing memory footprint by dropping transient data structures from memory, making them beneficial for handling less frequently queried historical data. The use of frozen indices is highlighted as an innovative feature in Elastic Stack, allowing businesses to retain searchable historical data without incurring significant memory overhead or operational costs associated with traditional methods like data snapshots and archiving. The process involves using the Freeze index API, ensuring indices are force-merged before freezing to enhance performance, and integrating with tools like Kibana for visualization. Additionally, the text outlines the operational nuances of managing frozen indices, including the necessity of unfreezing them for write operations and configuring search settings in Kibana to access frozen data.

Cross-cluster replication (CCR) has been introduced in Elasticsearch 6.7.0, allowing for seamless data replication between Elasticsearch clusters, addressing critical use cases such as disaster recovery, data locality, and centralized reporting. Previously dependent on third-party solutions, CCR is now natively integrated, offering benefits like comprehensive error handling and simplified management via Elasticsearch APIs and Kibana UIs. CCR operates on an active-passive index model, where a "follower index" replicates changes from a "leader index" at the shard level, ensuring efficient, near-real-time data replication without additional load on the leader. This capability enhances the ability to maintain high throughput and scale with production loads, supporting both new and existing indices, and incorporating features like auto-follow patterns and integration with index lifecycle management (ILM) for efficient management of time-based indices. The underlying technology of CCR, including "soft deletes" and shard history retention leases, ensures robust replication by preserving a history of operations and controlling when deleted documents are merged away, which is vital for maintaining synchronization between leader and follower indices.

Imma Valls, an engineering consultant at Elastic, volunteers her time with the LECXIT program in Catalonia, Spain, helping children aged eight to twelve improve their reading skills. Through Elastic's Volunteer Time Off (VTO) policy, Imma dedicates an hour each week from October to June to mentor a child, offering a range of creative and engaging activities like building Lego robots and making origami to foster a love of reading. Her involvement with LECXIT was inspired by her passion for reading and the encouragement of a friend, and she finds the experience rewarding both for herself and the children, as it not only enhances their reading abilities but also boosts their self-esteem. Elastic's support for diverse volunteer activities allows employees like Imma to make a meaningful impact in their communities while enriching their own lives.

The Elastic Uptime Monitoring solution, introduced in Elastic Stack 6.7, offers a comprehensive tool for tracking service uptime and response time across various network vantage points using Heartbeat, an open-source uptime monitor. This solution enables organizations to measure, report, and alert on service availability, helping operations teams manage the complexities of interconnected applications and microservices. By integrating with the Elastic Stack, Uptime Monitoring combines data from logs, metrics, and APM trace data to facilitate observability, troubleshoot issues, and perform root cause analysis. Heartbeat runs periodic checks on configured endpoints to deliver updates to Elasticsearch, which can be visualized and analyzed in Kibana, and allows for the creation of alerts using Watcher for proactive issue detection. This release marks the beginning of a broader observability initiative by Elastic, promising future enhancements for monitor management and configuration.

Version 6.7 of the Elastic Stack marks a significant release, introducing new features and graduating several to General Availability (GA) status. Notable additions include Elastic Maps, which enhances geospatial data visualization in Kibana, and Elastic Uptime, a solution based on Heartbeat for monitoring the uptime of services and applications. Elasticsearch 6.7 sees Cross Cluster Replication, Index Lifecycle Management, and Elasticsearch SQL all achieving GA status, while Kibana introduces Canvas for data visualization and gains its first localization in Simplified Chinese. Functionbeat, a serverless data streaming solution, is now GA, alongside the Infrastructure and Logs solutions. The release also includes an Upgrade Assistant to facilitate a smooth transition to the upcoming 7.0 version. These enhancements aim to improve data management, visualization, and operational efficiency across the Elastic Stack.

Kibana 6.7.0 introduces several new features and improvements, including support for Simplified Chinese through a new localization framework, a beta version of Maps for geospatial analysis with enhanced features, and the ability to manage follower indices in cross-cluster replication. Index Lifecycle Management (ILM) sees enhancements with the ability to freeze and unfreeze indices, set recovery priorities, and has moved out of beta into production. The Canvas and Infrastructure apps are now production-ready, offering improved performance, stability, and functionality for managing assets and infrastructure metrics. Additionally, the Logs app and a new Uptime feature for monitoring application status are now generally available. The release also reintroduces the Upgrade Assistant to facilitate migration to Elasticsearch 7.0, with improvements in the reindexing process. Users are encouraged to explore these features through the Elasticsearch Service or by downloading the stack.

Elastic Maps, introduced as a beta feature in version 6.7 of the Elastic Stack, offers a new way to map, visualize, and query location data within Kibana, significantly enhancing the existing region and coordinate maps visualizations. It allows users to add multiple layers from various data sources, including Elasticsearch documents, self-hosted vector layers, and image services, enabling complex geospatial analysis with data-driven styling capabilities. Users can map individual documents, apply real-time data filtering, and customize tooltips for detailed insights. Elastic Maps supports the integration of additional data sources, facilitating the creation of comprehensive visual representations, such as mapping zip codes with aggregated business metrics. Looking towards a general availability release, the development team is focusing on features like full embeddability and GeoJSON upload support, while encouraging user feedback and engagement through resources like webinars and the Kibana GitHub repository.

Elasticsearch for Apache Hadoop (ES-Hadoop) 6.7.0 has been released, aligning with Elasticsearch 6.7.0, and introduces several updates, including the deprecation of the Cascading integration due to declining usage and the high cost of maintenance. The deprecation decision is based on reduced community engagement and the focus on supporting Cascading 2, while Cascading itself has moved to a 3.X line with minimal pressure to upgrade. A notable new feature in this release is the support for Kerberos authentication, allowing secure connections to Elasticsearch clusters using Kerberos credentials, which is applicable to all integrations with additional support for managing credentials in long-lived Storm topologies and Spark Streaming jobs. Users are encouraged to consult the new documentation for configuring Kerberos and to provide feedback or suggestions via forums or GitHub.

Elastic has announced the general availability of Elastic Logs, a tool designed to streamline the process of working with logs using the Elastic Stack. The platform requires minimal data input, specifically a @timestamp and message fields, to facilitate real-time streaming and historical log searches. Users can improve their log analysis with structured queries through the Logs UI, which supports auto-complete functions. The integration with Filebeat, an open-source log shipper, enhances the process of receiving structured logs, while the Source Configuration UI allows for customizable settings. The latest update also introduces serverless log shipping support with Functionbeat, initially available for AWS Lambda, and includes improvements such as a "Log event document details" flyout for examining metadata. Elastic's broader observability suite now includes general availability for Infrastructure and Uptime solutions, complementing its APM offering. Users are encouraged to engage with Elastic for feedback and suggestions as the platform continues to evolve.

Logstash 6.7.0 introduces several enhancements, including support for Java 11, transitioning the native Java plugin API from experimental to beta, and a JRuby upgrade to version 9.2.6.0, which supports Ruby 2.5 and includes various bug fixes. The release allows Logstash to operate with both Java 8 and 11, enabling Java developers to create plugins using Java code without relying on Ruby, enhancing interoperability and reducing execution overhead. Key improvements to bundled plugins include a timeout enforcer for the KV filter, enhanced error handling via a new `tag_on_failure` directive, direct conversion capabilities in the Mutate filter, and added options for managing Elasticsearch and HTTP inputs. Additionally, the Elasticsearch output now supports automatic enabling of Index Lifecycle Management based on cluster support, reflecting a focus on optimizing performance and usability.

Elasticsearch 6.7.0, based on Lucene 7.7.0, introduces several significant features now available for production use, including Cross Cluster Replication (CCR), SQL interface, and Index Lifecycle Management (ILM), all of which have reached general availability. CCR facilitates data replication across data centers and regions, enhancing data accessibility and centralized reporting. The SQL interface allows seamless access to Elasticsearch data using SQL, expanding compatibility with BI tools, while ILM simplifies managing data lifecycle stages, including frozen indices for long-term storage. The release also supports rolling upgrades to Elasticsearch 7.x, enhanced Upgrade Assistant features, and improved reindexing processes. Additionally, new security and index management features, such as custom authorization engines and bundled geo-ip and user-agent plugins, further enrich Elasticsearch 6.7.0's functionality, offering users enhanced data management and security capabilities.

Elasticsearch 7.0 introduces a new real memory circuit breaker to enhance node resiliency by using JVM functionality to accurately measure memory usage, rather than relying on estimates from existing circuit breakers. This approach aims to prevent nodes from running out of memory by rejecting requests that would surpass the memory limit, improving Elasticsearch's ability to handle significant loads and sustain workloads that were previously problematic. The real memory circuit breaker allows the system to push back against memory-intensive requests, providing feedback to clients for implementing backoff and retry mechanisms. Experiments have shown that this new implementation significantly improves Elasticsearch's stability under various conditions, making it more robust against memory overloads and enhancing its capacity to handle peak workloads.

Elastic Infrastructure has officially reached general availability, offering users a comprehensive solution for monitoring infrastructure metrics with the Elastic Stack. This release builds upon user feedback and includes significant enhancements such as a streamlined Source Configuration UI, a neutral color gradient for more versatile metric visualization, and the ability to group infrastructure components by any field or tag in the Map view. Additionally, a new Table view allows for easy sorting of infrastructure components by key attributes. The 6.7 release also expands support for various Metricbeat modules, including those for MariaDB, Percona, and components from the Kubernetes ecosystem, as well as other widely-used technologies, now moved to general availability. The release forms part of a broader observability strategy that includes general availability for Logs and Uptime solutions, aiming to provide a seamless and robust infrastructure monitoring experience with the Elastic Stack.

Founded in 2014, Liefery is a Berlin-based delivery company that aims to provide transparent and plannable same-day and next-day delivery services. The company has enhanced its business intelligence capabilities by adopting the Elastic Stack, a decision driven by the need to improve log analytics and operational metrics. Initially struggling with the cumbersome process of analyzing log files, Liefery found the Elastic Stack's combination of Elasticsearch and Kibana to be a powerful and flexible solution for data visualization. This integration allowed them to create detailed dashboards for IT and operational quality, displayed on screens throughout the workplace, facilitating improved service quality and more efficient meetings. The Elastic Stack has also enabled Liefery to implement business alerting and extend its software offerings as a SaaS platform with powerful analytics features. The adoption of Elastic has resulted in significant process improvements, making both the business and tech teams more agile and responsive.

WordPress, a widely-used open-source content management system, now offers an enhanced search experience through the Elastic Site Search WordPress Plugin, which integrates with Elasticsearch. This plugin replaces the default WordPress search functionality with a more robust and configurable search experience, allowing users to easily implement advanced features such as typo-tolerance, partial word matching, and phrase matching. Users can quickly get started by installing the plugin, providing an API key, and synchronizing their content, which is indexed and transformed into searchable documents. The plugin supports 13 different languages and offers refined search relevance through hands-on tuning via its polished user interface, enabling the construction of faceted queries and management of result rankings and synonyms. Additionally, it provides rich search analytics, offering actionable insights to improve the search experience on various types of websites, from online stores to knowledge bases. A free 14-day trial is available for users to explore its capabilities without altering any code.

NS1, a prominent DNS host and web traffic management company, faced challenges in managing large volumes of telemetry data due to the limitations of their initial dual-system architecture comprising an OpenTSDB cluster for metrics and an Elasticsearch cluster for log data. As their operations scaled to processing terabytes per day, NS1 decided to consolidate their telemetry systems by adopting Elasticsearch as their primary time series database (TSDB). This transition allowed them to handle both structured and unstructured data more effectively while utilizing the Kibana UI for data analysis. The decision was influenced by Elastic's robust community support and the comprehensive features of Elasticsearch, including its clustering capabilities, which provided fault tolerance and easy scalability. Christian Saide, a DevOps and Software Engineer at NS1, highlighted the significant time and resource savings achieved through this integration, as well as the enhanced ability for data introspection and problem-solving. Despite the learning curve associated with this overhaul, the adoption of Elasticsearch has been a successful and transformative solution for NS1, enabling them to focus more on their core business systems.

Elastic Engineering provides guidance for new contributors on how to effectively file GitHub issues for the Elastic Stack, covering bug reports, feature requests, and code or documentation changes. The process begins with ensuring the use of the latest version of the Elastic Stack to avoid duplicating previously resolved issues. Contributors are encouraged to search existing issues to prevent redundancy and to join ongoing discussions if applicable. When submitting a new bug, they should provide detailed descriptions and test cases, often using curl commands from Kibana dev tools. For feature requests, specificity in describing the use case and desired functionality is crucial, as many current features originated from user suggestions. The guide also highlights the importance of collaboration when submitting code or documentation changes, urging contributors to engage in preliminary discussions with project teams to refine their contributions. The overarching goal is to demystify the submission process and encourage active participation from the community, while additional resources are available in each product's CONTRIBUTING.MD file.

Starting with version 7.0, Metricbeat introduced a new AWS module designed to enhance the monitoring of Amazon EC2 instances by collecting metrics from AWS Cloudwatch without requiring Metricbeat to be installed directly on each instance. This allows users to obtain high-level visibility into resource usage across regions, addressing the challenge of central monitoring. Cloudwatch provides basic and detailed monitoring options, which Metricbeat can leverage to collect EC2 metrics at varying granularities, with a default collection period of 300 seconds or 60 seconds for detailed monitoring. Setting up Metricbeat involves configuring AWS credentials, creating a specific IAM policy, and using the Kibana dashboard to visualize metrics, enabling users to optimize resource usage and address potential performance issues. The AWS module's EC2 metric set is part of a broader effort to expand Metricbeat's capabilities to monitor other AWS services like Amazon S3 and RDS, with ongoing development and community engagement encouraged through the Beats forum and GitHub repository.

CERDEC/ARL, a leading cyber defense entity within the U.S. Department of Defense, has adopted Elastic Cloud Enterprise (ECE) to enhance its capabilities in countering sophisticated cyber threats. Tasked with overseeing the vast network and system monitoring for the DoD, CERDEC/ARL required a scalable and reliable platform to support its extensive data needs and agile threat-hunting processes. ECE enabled improvements in policy enforcement, anomaly detection, and search visualization, allowing for rapid response in critical security scenarios. The implementation of ECE also streamlined the data ingest architecture, facilitating better data centralization and distribution for effective monitoring of cloud usage and potential insider threats. With enhanced search and indexing capabilities, alongside Kibana visualizations, CERDEC/ARL improved its incident response and communication of security states, positioning itself as a leader in defensive cyber operations.

The blog post provides a detailed guide for upgrading an Elasticsearch 1.7 cluster hosted on Elasticsearch Service to a more current version, emphasizing the importance of updating due to new features, security patches, and performance improvements available in newer versions. Elasticsearch 1.7 reached its end of life 26 months prior, and the service will discontinue support for this version after April 8, 2019. The recommended upgrade path involves first moving to version 5.6.x, with a further upgrade to 6.x advised to avoid dealing with another end-of-life version soon. The post outlines the process of logging into the Elasticsearch Service console, creating a new deployment, and reindexing data, including handling breaking changes such as updates to string data types. For those unable to follow the suggested upgrade path, an alternative is to move data off the service and manage their own Elasticsearch cluster, though this is not recommended due to security concerns. The post also highlights the significant new functionalities added to the Elastic Stack since version 1.7 and encourages users to explore these innovations or participate in the 7.0 Elastic Pioneer Program.

Elastic Stack (commonly known as ELK Stack) is a popular platform for storing and analyzing logs, offering two main approaches: "schema on read" and "schema on write." Schema on read allows users to apply structure to logs at search time, enabling ad hoc exploration but requiring repeated field extraction for ongoing reporting and dashboarding. Conversely, schema on write structures logs at the time of ingestion, which speeds up queries and aggregations by storing logs in a structured format, providing significant advantages for historical queries, real-time anomaly detection, and metrics correlation. This method also allows for better data quality verification, granular access control, and potentially lower storage needs by filtering unnecessary data upfront. The choice between these approaches depends on specific use cases and resource considerations, with Elastic Stack offering support for both methods to optimize workflows for different logging needs.

Katy Sue Wright, the Director of Elastic{ON} Events at Elastic, oversees the planning and execution of approximately 30 events annually, including the Elastic{ON} User Conference and its Tour events. These events are designed to engage users by providing product updates, answering questions, and fostering community, with the Tour events being particularly effective in reaching diverse cultures by offering localized, single-day programs. Wright's path to becoming an events expert was unconventional; she initially aspired to be a lawyer but found her passion for event planning through organizing large gatherings during her youth and coordinating events in a sorority. Her role allows her to travel extensively and connect with Elastic's global teams, enhancing her understanding of different cultures and building a strong sense of community within the company. Her favorite events include memorable moments like hosting a keynote in Dutch at an Amsterdam Tour stop, which highlighted the unique opportunities her position affords. She values the familial atmosphere at Elastic, which contrasts with the large corporate environments she feared when starting her career.

March 14, 2019, marks the release of the third iteration of Elastic App Search beta, known as Beta3, which introduces several new features and improvements. Users can now configure a mailer for sending invitations and create a compressed diagnostic bundle for troubleshooting. Noteworthy enhancements include the removal of the sample Engine, improved indexing performance, and configuration management through an appsearch.yml file. Additionally, multi-query curations have been updated to lift restrictions on active curated queries. The release encourages users of previous betas to upgrade and invites feedback as the product moves closer to general availability, with a call for new users to experience the free test drive of Elastic App Search.

Aginic, a leading data analytics consulting agency in Australia and an Elastic partner, showcased the capabilities of Elastic Stack's new Canvas and SQL features at an Elastic Brisbane meetup. Their team used open data sources, such as the NYC CitiBike System, to create hypothetical dashboards aimed at enhancing bike sharing in New South Wales. By integrating transactional trip data and point-in-time station status metrics into Kibana's Canvas, they demonstrated the potential for improving bike rental rates, inventory monitoring, and commuter satisfaction. The Canvas dashboard allows for creative, object-centric visualizations that facilitate rapid iteration and meaningful data representation, catering to both system maintainers and public users. This innovative approach to dashboard design holds promise for enhanced user interaction and uptake, filling a gap left by traditional business intelligence software and offering new opportunities for design thinking and agile iteration in business intelligence contexts.

Elasticsearch 7.0 introduces a revamped cluster coordination subsystem that enhances scalability, safety, and ease of use compared to earlier versions. This new subsystem replaces the Zen Discovery method and removes the need for the minimum_master_nodes setting, instead allowing Elasticsearch to automatically manage quorum settings and voting configurations, thus reducing the risk of misconfigurations that could lead to data loss. The coordination system ensures consistent cluster state updates across nodes, even amidst failures, by electing a master node and using quorum-based acceptance of updates. It also offers improved fault tolerance, faster master elections, and clear logging for troubleshooting. The upgrade from version 6.x to 7.0 can be done via a rolling upgrade or full-cluster restart, with the former allowing for continuous cluster availability. The redesign aligns more closely with distributed consensus theories, ensuring both safety and liveness, while maintaining compatibility with existing Elasticsearch behavior through advanced fault detection and adaptive techniques. The enhancements facilitate zero-downtime upgrades and make cluster management more robust and intuitive.

Elastic, founded by Shay Banon, is dedicated to building exceptional products and fostering strong communities around open-source software, beginning with the creation of Elasticsearch in 2009. Despite facing fear, uncertainty, and doubt (FUD) from larger companies and seeing their products forked and replicated, Elastic has maintained focus on innovation and community trust. This commitment is reflected in their open-source ethos, commercial transparency, and strategic partnerships, such as their collaboration with APM company OpBeat. Elastic continues to prioritize open-source development, resisting preferential treatment requests from larger entities like Amazon, to ensure every contributor is valued equally. Their philosophy emphasizes staying true to their mission and users, asserting that maintaining focus amidst distractions is essential for success.

Lenovo, a major global hardware manufacturer, faced challenges with their MySQL database in managing the vast number of logs generated daily, which hindered their ability to develop new business initiatives such as in-store research programs. By adopting Elasticsearch, Lenovo's small BT/IT team, led by Liuxue Cao, was able to efficiently handle over 400 million logs per day, freeing them to focus on projects that provide business insights crucial for decision-making. This transition allowed Lenovo to analyze consumer interactions with products in real time across 5,000 retail and partner stores in China, offering insights into customer preferences and optimal product placement. The data gathered enables Lenovo to make informed inventory adjustments and develop new Elasticsearch-powered initiatives that link product sales to real-time demo interactions, ultimately enhancing their understanding of consumer behavior and guiding future product development.

Oak Ridge National Laboratory (ORNL), home to the world's fastest supercomputer, Summit, transitioned from using Splunk to Elasticsearch for their cybersecurity needs, significantly enhancing their capacity to manage security information and event management (SIEM) for approximately 20,000 endpoints. The switch was motivated by the limitations of Splunk's data ingestion costs and slow search speeds, which hampered the lab's ability to manage vast data volumes and conduct timely analyses. With Elasticsearch, ORNL has achieved faster data processing and eliminated ingestion limitations, deploying a robust architecture that includes 25 Elasticsearch nodes across virtual machines, enabling them to ingest over two billion documents daily. The Elastic Stack, including tools like Kibana and Graph, further enhances their cybersecurity operations, allowing comprehensive monitoring and rapid response to potential threats. The lab has also employed Canvas to create interactive dashboards for management, providing high-level overviews of security activities.

Elastic, through its Elastic Cares program, empowers employees, known as Elasticians, to engage in philanthropic efforts by offering Volunteer Time Off, donation matching, and support for non-profit organizations. Highlighting two significant initiatives, the program facilitated campaigns for Breast Cancer Awareness and Trans Rights. Kristina Frost shared her personal journey with breast cancer, emphasizing the company's support and the success of a fundraising campaign that raised nearly $11,000 for research foundations. Kiley Davidson recounted her experience as a transgender employee, detailing how the Elastic Cares program helped launch a campaign that raised over $20,000 for transgender advocacy organizations, fostering a sense of belonging and safety within the company. These narratives underscore Elastic's commitment to inclusivity and support for diverse causes, encouraging employees to champion initiatives that resonate with them personally.

NewCo is a festival series founded by John Battelle that celebrates innovation and transformation in the business world by organizing city-specific events across North America, Europe, and Latin America. These festivals serve as a platform to connect entrepreneurs and businesses, fostering a community of inspiration and learning. Elastic is participating in NewCo London 2019, opening its Covent Garden office to the public on March 14 to showcase its work in search and security analytics and engage with attendees. The event will feature discussions from Elastic's team, including Territory Manager Dean Prosenica, Client Success Manager Jared Hall, and Machine Learning Lead Sophie Chang, highlighting collaborations with companies like Netflix, Tinder, and Uber. Elastic emphasizes the importance of collaboration and community engagement to drive innovation and address consumer and organizational needs, aligning with NewCo's mission to connect agents of positive change in society.

Elastic App Search harnesses the power of search analytics to enhance application performance by offering insights into user intent and behavior. Through its API, developers can access data on search queries and clicks, enabling them to identify popular search terms and documents, which can be leveraged to automatically optimize content placement within applications. This process is exemplified by an ecommerce scenario, where popular items, like unexpectedly trending shoes, are dynamically highlighted based on search data, thus improving user experience and engagement. Additionally, the use of tagging allows for further personalization by filtering analytics based on user context, such as device type. By utilizing these capabilities, applications can provide more relevant and efficient search experiences, ultimately leading to higher user satisfaction and business outcomes. Elastic App Search supports various programming languages and offers both hosted and self-managed versions, allowing developers to experiment and integrate these functionalities into their applications effectively.

Bell Canada, a leading telecommunications company, faced challenges with their existing ArcSight Security Information and Event Management (SIEM) solution as it struggled to handle the growing volume and diversity of logs generated by various business units. To address these issues, Bell Canada's security operations center (SOC) augmented their SIEM with the Elastic Stack, which improved log handling and security data generation while reducing false positives. The SOC deployed tools like Filebeat and Winlogbeat for efficient log shipping, Kafka for queuing, and Logstash for parsing and normalizing logs, all within a containerized environment that allows for quick scaling. They use Elasticsearch for storing and managing logs, with a hot-warm architecture to balance the load, and Kibana for data visualization, which is user-friendly and requires minimal training. Additionally, they developed in-house machine learning solutions using open-source libraries for smarter threat detection, integrating with existing protocols to ensure seamless operation. The SOC plans to further enhance their infrastructure by incorporating a Cyber Threat Intelligence platform, demonstrating their commitment to robust security and data management.

Building a Searchbot that integrates Slack, Zapier, and Elastic App Search enables users to query vast amounts of data directly from Slack channels without coding. This process involves setting up an Elastic App Search account to harness its robust search capabilities, such as relevance tuning and search analytics, and linking it with Slack via Zapier, an automation platform. By creating a series of automated steps, or "Zaps," users can set up triggers in Slack that send search queries to Elastic App Search as API calls, and return formatted results. This setup, which takes roughly 30 minutes, allows users to search through diverse datasets, such as customer records or server logs, by simply typing queries in Slack, making information retrieval efficient and streamlined.

The process of contributing a plugin to the Elastic APM Java agent involves understanding its open-source nature, which allows for the extension of its capabilities through community contributions. The Elastic APM agent can trace specific methods in third-party libraries using a public API and custom-method-tracing configuration, but for more detailed monitoring, contributors can develop plugins by following certain guidelines. The article outlines the steps to instrument the Elasticsearch Java REST client, highlighting the importance of selecting the right methods for instrumentation, designing code using Maven, and implementing it while considering class visibility, performance overhead, and concurrency issues. It emphasizes the use of the Byte Buddy library for bytecode manipulation and advises on creating efficient, non-intrusive code that minimizes overhead. The piece also stresses the significance of community feedback and collaboration to enhance the agent's functionality, encouraging potential contributors to engage with the Elastic APM team via forums or GitHub.

On February 4, 2019, Elastic Cloud experienced significant service disruptions in the AWS us-east-1 region, affecting Elasticsearch and Kibana access for customers due to issues during a routine patching procedure of the coordination layer's ZooKeeper ensemble. The incident led to degraded performance and outages from 02:50 to 09:28 UTC for Elasticsearch and until 18:44 UTC for Kibana, caused by a combination of high load, instability in the ZooKeeper observer layer, and internal TLS proxying service errors. The root cause was traced to unfamiliar errors encountered during maintenance, compounded by insufficient service metrics and a set of unexpected issues. Resolution involved re-establishing quorum in the ZooKeeper ensemble, stabilizing the observer layer, and addressing Kibana's connection leaks and request amplification. Elastic Cloud has since implemented various engineering and process improvements to enhance system resilience and prevent future incidents, including reducing ZooKeeper data size, optimizing proxy health-check logic, and improving internal logging and monitoring strategies.

The 2019 Elastic Search Awards celebrated innovative uses of the Elastic Stack across three categories: Cause Awards, Cluster Awards (Technology Innovation), and You Know, for Search! Awards (Business Transformation). Honorees included GuideStar by Candid and NowPow for their impactful contributions to the nonprofit and healthcare sectors, respectively, using Elastic technology to enhance search capabilities and resource matching. SolveBio and Jet.com were recognized for their technological innovations in genomics and ecommerce, leveraging Elasticsearch to handle complex data and visual search systems. T-Mobile USA and DATASUS, associated with the Brazilian Ministry of Health, received accolades for transforming business operations by using Elastic Stack to personalize customer experiences and manage national health data. The awards highlighted the versatility and creativity in applying Elastic's tools to solve diverse challenges, as noted by the judges during the Elastic{ON} Tour San Francisco announcement, where honorees were acknowledged for their outstanding contributions.