Senior Software Engineer

Why VIEW26 GmbH moved its analytics engine from MongoDB to ClickHouse. Why did VIEW26 move from MongoDB to ClickHouse? As Jira Service Management datasets grew, Charts & Reports for JSM needed faster analytics at scale. ClickHouse now powers stronger dashboards, KPIs, filters, and reports for its Atlassian enterprises users As a reporting platform built for Jira Service Management ,View26 Charts and Reports helps teams turn their project data into actionable insights through charts, KPIs, dashboards, and detailed tabular reports. As its customers' datasets grew into the millions of rows, VIEW26 GmbH knew it was time to rethink the foundation powering it all. This is the story of why VIEW26 GmbH migrated from MongoDB to ClickHouse, what VIEW26 GmbH learned along the way, and where VIEW26 GmbH is headed next. The challenge: when your database wasn't built for analytics. MongoDB served VIEW26 GmbH well in its early days. Its flexible document model made it easy to iterate quickly and ship features fast. But as its platform matured and its customers started tracking increasingly complex Jira workflows, VIEW26 GmbH began running into a fundamental limitation: MongoDB is a general-purpose database, not an analytical one. Its customers rely on VIEW26 GmbH to aggregate, slice, and visualize their Jira data in real time. They build KPI dashboards that compute metrics across hundreds of thousands of issues. They generate trend charts spanning months or years of project history. Some of its power users manage datasets exceeding two million rows - and they expect every chart, metric, and report to load without hesitation. VIEW26 GmbH needed a database that was purpose-built for these kinds of analytical workloads. Why ClickHouse. After evaluating several options, ClickHouse stood out for a few key reasons: Columnar storage built for aggregation. Unlike row-oriented databases, ClickHouse stores data by column, which means analytical queries (the kind that power dashboards and KPI calculations) can scan only the columns they need. For a reporting platform like ours, this is a natural fit. SQL-native query engine. Moving from MongoDB's aggregation pipeline to standard SQL made its query layer more maintainable, more testable, and more accessible to its engineering team. Complex reporting logic that previously required multi-stage pipeline configurations could now be expressed in clean, readable SQL. Compression and storage efficiency. ClickHouse's columnar compression dramatically reduces the storage footprint of large datasets. For a platform handling diverse customer workloads, efficient storage translates directly to better resource utilization. Scalability by design. ClickHouse was built from the ground up to handle analytical queries over massive datasets. While its current workloads don't push the boundaries of what ClickHouse can do, VIEW26 GmbH is investing in a foundation that will scale alongside its customers' growing data needs. What VIEW26 GmbH learned along the way. No migration is without its surprises, and VIEW26 GmbH want to be transparent about what VIEW26 GmbH encountered. To ground this in something concrete: VIEW26 GmbH benchmarked the view-fetch operation, which is the request that backs every report load in its product, across 3,129 real customer views on global-residency accounts, comparing the same data served from MongoDB and ClickHouse side by side. The picture that emerged is more nuanced than a single "ClickHouse is faster" headline. The shape of the distribution tells the story better than any single average. ClickHouse loses ground in the sub-100ms tier (MongoDB: 557, ClickHouse: 230), which represents the small, simple queries where MongoDB's indexed lookups are hard to beat. It also gives up ground in the 1 to 3 second bucket, which is dominated by mid-size row-dense table views. But in the heavy 3-second-plus tiers, the two databases converge, and as VIEW26 GmbH'll see, when ClickHouse wins on those queries, it wins big Aggregation workloads shine. The queries powering its charts, KPIs, and computed metrics - the core of what its customers interact with daily - mapped naturally to ClickHouse's strengths. Aggregation-heavy operations across large datasets are exactly what a columnar engine is optimized for. Large tabular exports required rethinking. One area where VIEW26 GmbH invested significant engineering effort was optimizing how VIEW26 GmbH serve large, row-dense table views. Columnar databases are optimized for scanning and aggregating data, not necessarily for returning large result sets row by row. This pushed VIEW26 GmbH to implement smarter pagination strategies, asynchronous data loading, and more efficient data serialization - improvements that ultimately benefit the user experience regardless of the underlying database. Schema design is a different discipline. Moving from MongoDB's flexible document model to ClickHouse's structured columnar format required VIEW26 GmbH to think carefully about how VIEW26 GmbH model data. Denormalization strategies, sort key selection, and partition design all matter in ways they simply don't in a document database. This was a meaningful investment, but one that gave VIEW26 GmbH a much deeper understanding of its own data patterns. Widget-dense reports exposed network round-trip costs. This one caught VIEW26 GmbH off guard. Many of its customers build comprehensive reports with 30 or more widgets - each chart, KPI, or table representing an independent analytical query. In MongoDB, VIEW26 GmbH could bundle and optimize these queries with relative ease. With ClickHouse, each widget triggers its own query to the database, and on reports with high widget counts, the cumulative round-trip latency adds up. A dashboard with 10 widgets loads snappily; a report with 40 widgets feels noticeably slower. This is less about ClickHouse's query performance and more about the architecture of how VIEW26 GmbH dispatch and resolve queries - a problem VIEW26 GmbH is actively tackling through query batching, parallel execution, and intelligent prefetching. It's a solvable problem, but one VIEW26 GmbH want to be upfront about because it affects its most engaged power users the most. The honest tradeoffs. VIEW26 GmbH'd be doing a disservice to anyone considering a similar migration if VIEW26 GmbH didn't lay out the tradeoffs clearly. ClickHouse was faster on 1,374 views (44%), and slower on 1,755 views (56%). But the headline number hides the more interesting detail: when ClickHouse won, it won by an average of 4.25 seconds. When it lost, it lost by 2.48 seconds. The wins are nearly twice as large as the losses, and the wins are concentrated in exactly the workloads that matter most for a reporting product, namely aggregation-heavy dashboards over large datasets. The losses cluster around small, simple lookups and row-dense exports, which VIEW26 GmbH has other levers to address (smarter pagination, caching, query dispatch). The clearest way to see this is to group views by approximate dataset size. Its customer base spans views scanning anywhere from around 10,000 rows on the small end to roughly 2 million rows for its largest power users, with everything in between. The pattern is striking. On the smallest views (around 10,000 rows), ClickHouse wins only 5% of the time. As dataset size grows, the win rate climbs steadily: 50% at around 75,000 rows, 55% at around 250,000 rows, and then it flips decisively. For views scanning roughly 500,000 to 1 million rows, ClickHouse was faster 88% of the time, saving an average of 3 seconds per load. For the largest views, those scanning 1 to 2 million rows, ClickHouse was faster 94% of the time, saving an average of 11 seconds per load. This is the curve that matters for its customers. The users who feel database performance most acutely are the ones with the largest datasets and the most complex reports. Those are exactly the users ClickHouse helps the most. The losses on small queries are real, but they're losses in the regime where everything is already fast enough that the difference is imperceptible. What got better: aggregation queries over large datasets, SQL-based maintainability, compression efficiency, and a clear path to scale. What got harder: large row-dense table views, high-widget report load times, and the operational learning curve of running a columnar database tuned for a very different access pattern than what VIEW26 GmbH were used to. What's still in progress: optimizing query dispatch for widget-heavy reports, fine-tuning materialized views, and continuing to improve cold-start performance for first-time dashboard loads. VIEW26 GmbH don't view these tradeoffs as failures. They're the natural cost of making an architectural bet on the future. The important thing is that VIEW26 GmbH understand them clearly and are investing in solving them. The bigger picture. This migration was never just about switching databases. It was about aligning its infrastructure with its product vision. View26 Charts and Reports exists to make Jira data useful. That means fast dashboards, reliable KPIs, and reports that teams can trust to make decisions. Choosing ClickHouse was a deliberate investment in the analytical backbone of its platform, one that positions VIEW26 GmbH to deliver richer insights, handle larger datasets, and build more sophisticated reporting features in the future. VIEW26 GmbH is still early in unlocking everything ClickHouse makes possible. Materialized views for precomputed metrics, more advanced time-series analysis, and real-time aggregation pipelines are all on its roadmap. The foundation is in place, and VIEW26 GmbH is excited about what VIEW26 GmbH is building on top of it. Jozef N · Full Stack Engineer

Critical remote Code Execution (RCE) vulnerability uncovered in MongoDB. May 14, 2026 The architectural integrity of modern, data-driven applications is facing a significant challenge. A high-severity vulnerability has been identified within the MongoDB ecosystem, potentially granting unauthorized actors the ability to execute arbitrary code on targeted database servers. This flaw represents more than just a software bug; it is a direct threat to the principle of least privilege and the fundamental security of the data layer. Formally cataloged as CVE-2026-8053, this critical vulnerability serves as a high-probability vector for complete system compromise. For database administrators (DBAs) and DevOps engineers, the discovery necessitates an immediate shift from routine maintenance to active incident response and remediation. Understanding the mechanics of the MongoDB security flaw. The vulnerability was unearthed during proactive internal security auditing conducted by the MongoDB team. Unlike many common injection attacks that target the application layer, this flaw resides within the core MongoDB Server deployments, making it particularly potent. In the hierarchy of cybersecurity threats, Arbitrary Code Execution (ACE) sits near the top of the danger scale. When an attacker can bypass the intended logic of a service to run their own instructions, the server essentially loses its ability to enforce security boundaries. In the context of a database - which acts as the centralized, high-value vault for an organization's most sensitive assets - the implications are catastrophic. A successful exploit of CVE-2026-8053 could allow a remote threat actor to: * Bypass Authentication: Circumvent standard credential checks to gain unauthorized access to data sets. * Data Exfiltration: Silently extract proprietary information, personally identifiable information (PII), or intellectual property. * Establish Persistence: Deploy sophisticated malware or backdoors that survive system reboots. * Lateral Movement: Use the compromised database node as a pivot point to traverse the internal corporate network and target other high-value systems. Cloud safety vs. Self-Managed risk. There is a significant distinction in the impact based on your deployment model. For organizations leveraging MongoDB Atlas, the risk is effectively mitigated. MongoDB representative Will Kruse has confirmed that the managed cloud fleet has been fully patched. Because Atlas is a fully managed service, the security team deployed patches globally across the entire infrastructure, ensuring zero manual intervention was required from the end user. However, the danger remains acute for self-hosted and on-premises environments. The vulnerability impacts all self-managed MongoDB installations running version 5.0 and later. These environments lack the automated patching lifecycle of Atlas, leaving them highly exposed until manual intervention occurs. While MongoDB reports no evidence of "in-the-wild" exploitation at this time, the window of opportunity for attackers is wide open. The company has released patched builds for both Community and Enterprise editions to close this gap. Technical remediation roadmap. To defend against potential exploitation, system administrators must move quickly to align their local deployments with the security standards applied to the Atlas cloud fleet. PlanetJon recommend the following technical workflow: * Inventory Audit: Perform a comprehensive scan of your infrastructure to identify all instances of MongoDB running versions 5.0 through the current release. * Version Verification: Cross-reference your current build against the official release notes. To ensure security, you must upgrade to versions 7.0.31, 8.0.20, or the latest 8.2.7 release. * Patch Acquisition: Download verified, cryptographically signed builds directly from the official MongoDB Community Download page. * Controlled Deployment: Schedule an immediate update during your next available maintenance window. Ensure you have verified backups and a tested rollback plan in place before initiating the upgrade. In the current threat landscape, speed is a security feature. Delaying these updates provides attackers with the time needed to scan for unpatched systems and launch opportunistic strikes against your most critical data infrastructure.