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Database Internals - Kotlin SDK

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  • Native Database Engine
  • Realm Files
  • Copy-on-Write: The Secret Sauce of Data Versioning
  • Memory Mapping
  • Compaction
  • ACID Compliance

Realm uses a completely unique database engine, file format, and design. This section describes some of the high-level details of those choices. This section applies to both the device-local version of Realm as well as the networked Device Sync version. Differences between the local database and the synchronized database are explained in the Atlas Device Sync section.

Realm is an entire database written from scratch in C++, instead of building on top of an underlying database engine like SQLite. Realm's underlying storage layer uses B+ trees to organize objects. As a result, Realm controls optimizations from the storage level all the way up to the access level.

Realm stores data in realms: collections of heterogeneous realm objects. You can think of each realm as a database. Each object in a realm is equivalent to a row in a SQL database table or a MongoDB document. Unlike SQL, realms do not separate different object types into individual tables.

Realm stores objects as groups of property values. We call this column-based storage. This means that queries or writes for individual objects can be slower than row-based storage equivalents when unindexed, but querying a single field across multiple objects or fetching multiple objects can be much faster due to spatial locality and in-CPU vector operations.

Realm uses a zero-copy design to make queries faster than an ORM, and often faster than raw SQLite.

Realm persists data in files saved on device storage. The database uses several kinds of file:

  • realm files, suffixed with "realm", e.g. default.realm: contain object data.

  • lock files, suffixed with "lock", e.g. default.realm.lock: keep track of which versions of data in a realm are actively in use. This prevents realm from reclaiming storage space that is still used by a client application.

  • note files, suffixed with "note", e.g. default.realm.note: enable inter-thread and inter-process notifications.

  • management files, suffixed with "management", e.g. internal state management.

Realm files contain object data with the following data structures: Groups, Tables, Cluster Trees, and Clusters. Realm organizes these data structures into a tree structure with the following form:

  • The top level, known as a Group, stores object metadata, a transaction log, and a collection of Tables.

  • Each class in the realm schema corresponds to a Table within the top-level Group.

  • Each Table contains a Cluster Tree, an implementation of a B+ tree.

  • Leaves on the Cluster Tree are called Clusters. Each contains a range of objects sorted by key value.

  • Clusters store objects as collections of columns.

  • Each column contains data for a single property for multiple instances of a given object. Columns are arrays of data with uniformly sized values.

  • Columns store data in one of the following sizes: 1, 2, 4, 8, 16, 32, or 64 bits. Each column uses one value size, determined by the largest value.

Since pointers refer to memory addresses, objects written to persistent files cannot store references as pointers. Instead, realm files refer to data using the offset from the beginning of the file. We call this a ref. As Realm uses memory mapping to read and write data, database operations translate these refs from offsets to memory pointers when navigating database structures.

Realm uses a technique called copy-on-write, which copies data to a new location on disk for every write operation instead of overwriting older data on disk. Once the new copy of data is fully written, the database updates existing references to that data. Older data is only garbage collected when it is no longer referenced or actively in use by a client application.

Because of copy-on-write, older copies of data remain valid, since all of the references in those copies still point to other valid data. Realm leverages this fact to offer multiple versions of data simultaneously to different threads in client applications. Most applications tie data refreshes to the repaint cycle of the looper thread that controls the UI, since data only needs to refresh as often as the UI does. Longer-running procedures on background threads, such as large write operations, can work with a single version of data for a longer period of time before committing their changes.

Writes use memory mapping to avoid copying data back and forth from memory to storage. Accessors and mutators read and write to disk via memory mapping. As a result, object data is never stored on the stack or heap of your app. By default, data is memory-mapped as read-only to prevent accidental writes.

Realm uses operating system level paging, trusting each operating system to implement memory mapping and persistence better than a single library could on its own.

Realm automatically reuses free space that is no longer needed after database writes. However, realm files never shrink automatically, even if the amount of data stored in your realm decreases significantly. Compact your realm to optimize storage space and decrease file size if possible.

You should compact your realms occasionally to keep them at an optimal size. You can do this manually, or by configuring your realms to compact on launch. However, Realm reclaims unused space for future writes, so compaction is only an optimization to conserve space on-device.

Realm guarantees that transactions are ACID compliant. This means that all committed write operations are guaranteed to be valid and that clients don't see transient states in the event of a system crash. Realm complies with ACID with the following design choices:

  • Atomicity: groups operations in transactions and rolls back all operations in a transaction if any of them fail.

  • Consistency: avoids data corruption by validating changes against the schema. If the result of any write operation is not valid, Realm cancels and rolls back the entire transaction.

  • Isolation: allows only one writer at a time. This ensures thread safety between transactions.

  • Durability: writes to disk immediately when a transaction is committed. In the event of an app crash, for example, changes are not lost or corrupted.

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