Ignite projects its partitioned in-memory caches as SQL schemas and tables. DbSchema connects with the thin JDBC driver, reads those cache-table mappings across the cluster, and renders them as an interactive ER diagram.
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An Ignite cluster holds its working data in partitioned caches across machines. Auditing that data model usually means querying system views or picking apart the cluster configuration XML — neither of which anyone volunteers to do.
DbSchema asks the SQL catalog instead, and turns the cache-backed tables into a diagram the whole team can read.
Desktop tooling for data that never touches a disk.
Build queries over cache-backed tables graphically — filters, grouping, joins — and read the SQL DbSchema generates before it hits the cluster.
Write collocated joins, distributed aggregations, and cross-cache queries in the editor — no separate Ignite client application, no shell session.
Generate documentation of the Ignite SQL schema — tables, columns, key mappings — for backend teams who consume the caches but never configured them.
Cache contents come and go with the cluster; the model file does not. Keep it in Git and the mapping of caches to tables gains what memory lacks — a history your team can diff, review, and reason about release after release.
DbSchema uses the Ignite JDBC thin driver, which
connects like any external tool — it starts no embedded Ignite node inside the client process.
The URL is jdbc:ignite:thin://host:10800/schema, with 10800 as the
default thin-client port; for a multi-node cluster, list the addresses for failover:
jdbc:ignite:thin://host1,host2:10800/schema. On connect, DbSchema lists the
cache-backed tables straight away.
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Teams working with Apache Ignite often use these engines too. Explore dedicated guides and JDBC setup for each.