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Realm Database - Node.js SDK

On this page

  • Database Internals
  • Native Database Engine
  • Realm Files
  • Copy-on-Write: The Secret Sauce of Data Versioning
  • Memory Mapping
  • Compaction
  • ACID Compliance
  • Features
  • Queries
  • Encryption
  • Indexes
  • Schemas
  • Persistent or In-Memory Realms
  • Realm Sync

Realm Database is a reactive, object-oriented, cross-platform, mobile database:

  • Reactive: query the current state of data and subscribe to state changes like the result of a query, or even changes to a single object.
  • Object-oriented: organizes data as objects, rather than rows, documents, or columns.
  • Cross-platform: use the same database on iOS, Android, Linux, macOS, or Windows. Just define a schema for each SDK you use.
  • Mobile: designed for the low-power, battery-sensitive, real-time environment of a mobile device.

Realm Database is an alternative to SQLite and Core Data.

This page explains some of the implementation details and inner workings of Realm Database and Realm Sync. This page is for you if you are:

  • a developer interested in learning more about Realm Database
  • comparing Realm Database with competing databases
  • trying to understand the difference between Realm Database and Realm Sync

This explanation begins with a deep dive into database internals, continues with a high-level introduction to some of the features of Realm Database, and wraps up with some of the differences between Realm Sync and the local version of Realm Database.

Realm Database 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 Database as well as the networked Realm Sync version. Differences between the local database and the synchronized database are explained in the Realm Sync section.

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

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

Realm Database 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. default.realm.management: internal state management.

Realm files contain object data with the following data structures: Groups, Tables, Cluster Trees, and Clusters. Realm Database 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 Database uses memory mapping to read and write data, database operations translate these refs from offsets to memory pointers when navigating database structures.

Realm Database 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 Database 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 Database 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 Database 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 Database reclaims unused space for future writes, so compaction is only an optimization to conserve space on-device.

Realm Database 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 Database 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.

Realm Database supports many popular database features.

You can query Realm Database using platform-native queries or a raw query language that works across platforms.

Realm Database supports on-device realm encryption. Since memory mapping does not support encryption, encrypted realms use a simulated in-library form of memory mapping instead.

Indexes are implemented as trees containing values of a given property instead of a unique internal object key. This means that indexes only support one column, and thus only one property, at a time.

Every realm object has a schema. That schema is defined via a native object in your SDK's language. Object schemas can include embedded lists and relations between object instances.

Each realm uses a versioned schema. When that schema changes, you must define a migration to move object data between schema versions. Additive schema changes happen automatically, but your SDK may require you to increase the local schema version to begin using the updated schema in your app. Destructive changes require a migration function. See your SDK's documentation for more information on migrations.

You can use Realm Database to store data persistently on disk, or ephemerally in memory. Ephemeral realms can be useful in situations where you don't need to persist data between application instances, such as when a user works in a temporary workspace.

Realm Sync adds network synchronization between a MongoDB Realm backend and client devices on top of all of the functionality of Realm Database. When you use Realm Database with Sync, realms exist on device just like when you only use Realm Database. However, changes to the data stored in those realms synchronize between all client devices through a backend MongoDB Realm instance. That backend also stores realm data in a cloud-based MongoDB Atlas cluster running MongoDB.

Realm Sync relies on a worker client that communicates with your application backend in a dedicated thread in your application. Additionally, synced realms keep a history of changes to contained objects. Sync uses this history to resolve conflicts between client changes and backend changes.

Applications that use Realm Sync define their schema on the backend using JSON Schema. Client applications must match that backend schema to synchronize data. However, if you prefer to define your initial schema in your application's programming language, you can use development mode to create a backend JSON Schema based on native SDK objects as you write your application. However, once your application is used for production purposes, you should alter your schema using JSON Schema on the backend.

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