Running Apache Jackrabbit should feel boring in the best way.
Here are the habits that keep a JCR repository fast, safe, and predictable.
Plenty of the buzz is around Oak and fresh releases of content platforms, yet many teams still rely on classic Apache Jackrabbit 2.x for production content stores, and it keeps doing the job when you respect its boundaries and tune the basics. Treat the repository home as a first class citizen on dedicated storage, and keep the Data Store on its own path, not tucked inside workspaces, so recovery is less dramatic and growth is easier to track. Use a real database for the Persistence Manager and the cluster journal, with PostgreSQL or MySQL or Oracle, and skip embedded Derby when money and sleep matter. Stick to a single workspace unless you have a very good reason, and shape your content tree with fan out that stays friendly to traversal, for example bucketing folders so you never collect tens of thousands of children under one parent. Design node types that keep large binaries in the Data Store and store only light metadata as properties, and be selective with versioning so you do not keep endless generations of heavy files that slow down reads and inflate backups. Keep observation handlers lean by passing events to an async queue in your app rather than doing heavy work on the event thread, and test listener throughput with the same volume and shape of content you expect in real life. If you build these habits from day one, Jackrabbit behaves like a tidy library that stays quiet no matter how many books you shelve each week.
Search is where silent defaults can bite, because every write has to feed the Lucene index and text extraction can become a brake if you throw giant PDFs at it during peak hours. Keep your SearchIndex settings honest about what you actually need: limit indexing to the node types that matter, avoid full text on binaries you never search, and tune Tika mappings so exotic formats do not clog extraction threads. Write queries that respect the tree: start from a path that narrows the scope, add property constraints that use indexed properties, avoid double slash scans that wander across the entire repository, and be careful with order by on huge result sets since sorting can dominate response time. Turn on query logging in lower environments, run explain on your most used queries, and record baseline timings so you can catch regressions when content grows or a new release ships. Keep an eye on extraction backlogs and the size of the index on disk, and plan ingestion windows so large binary imports happen when nobody is waiting at the front door, because indexing in Jackrabbit happens in step with content writes and you will feel it if you flood it at noon. Reindex only for a real reason such as configuration changes or index corruption, and do it on a clone in staging first so you learn how long it takes with your data shape. When queries are predictable and the index is sized right, your users will think search is simple, and that is the greatest compliment a repository can get.
Daily care is where teams save weekends, and Jackrabbit gives you knobs that repay the time you spend learning them, starting with backup routines that you can restore with your eyes closed. Use cold backups for the repository home paired with synchronized backups of the Data Store, and capture the two in the same window so references line up, especially in clusters. If you run clustering, remember each node has its own repository home while sharing the Data Store and the cluster journal, and do not put the whole repository home on a shared file system, because locking fights and latency will punish you; a database journal is usually the calmer choice. Run Data Store GC during quiet hours with a healthy margin of time, understand the mark and sweep phases, and keep a recent backup before you sweep so you can recover if a long running import was still referencing binaries when you started. After an unclean shutdown, run consistency checks and watch logs for orphan nodes or broken references before you open the gates to writers, and keep a playbook that lists the precise steps and commands so nobody has to guess while alarms are ringing. Monitor JVM memory, GC pauses, query latencies, pending text extraction, observation queues, and IO wait on the volumes that host the repository home and Data Store; Java 8 with a well sized heap and G1 or CMS tuned for your traffic can make Jackrabbit feel steady even under load. Secure your content with clear access control policies, prefer simple grants over deep deny rules, test with real group sizes, and keep service users scoped to the minimal paths they truly need. If you deploy with containers, mount durable volumes for both the repository home and Data Store, check inode usage since the Data Store creates many small files, and avoid rebuilding images with the repository inside so you do not lose state when a fresh pod spins up.
Keep Jackrabbit boring, and it will keep your weekends quiet.