realm-core

Pros of FASTER

• Designed for high-performance key-value operations with support for larger-than-memory data
• Offers hybrid log and in-memory index for efficient data management
• Provides both C# and C++ implementations for flexibility

Cons of FASTER

• Less mature and widely adopted compared to Realm Core
• Limited documentation and community support
• Narrower focus on key-value operations, while Realm Core offers a more comprehensive database solution

Code Comparison

FASTER (C#):

using (var session = store.NewSession(new SimpleFunctions<long, long>()))
{
    session.Upsert(1, 1);
    session.Read(1, out long value);
}

Realm Core (C++):

auto realm = Realm::get_shared_realm(config);
auto table = realm->read_group().get_table("MyTable");
realm->begin_transaction();
table->create_object().set("myColumn", 1);
realm->commit_transaction();

Summary

FASTER focuses on high-performance key-value operations, while Realm Core provides a more comprehensive database solution. FASTER offers potential performance benefits for specific use cases, but Realm Core has broader functionality and stronger community support. The choice between the two depends on the specific requirements of your project and the level of support and maturity you need.

Pros of FoundationDB

• Highly scalable distributed database system
• Strong consistency and ACID transactions across multiple machines
• Supports multiple data models through layered architecture

Cons of FoundationDB

• Steeper learning curve due to its distributed nature
• Less suitable for mobile or embedded applications
• Requires more resources to set up and maintain

Code Comparison

FoundationDB (C++):

Transaction tr(db);
tr.set(KeyRef("hello"), ValueRef("world"));
tr.commit().wait();

Realm Core (C++):

auto realm = Realm::get_shared_realm(config);
realm->begin_transaction();
auto dog = realm->create<Dog>("Rex");
realm->commit_transaction();

Key Differences

• FoundationDB is designed for distributed systems, while Realm Core focuses on local/mobile databases
• FoundationDB offers strong consistency across distributed setups, whereas Realm Core excels in offline-first scenarios
• Realm Core provides a more object-oriented API, while FoundationDB uses a key-value approach with optional layers

Both databases have their strengths, with FoundationDB being more suitable for large-scale distributed applications and Realm Core being ideal for mobile and embedded use cases.

Pros of LevelDB

• Simpler key-value store design, making it easier to use for basic storage needs
• Better performance for write-heavy workloads
• More mature and widely adopted in various projects

Cons of LevelDB

• Limited query capabilities compared to Realm Core's object-oriented model
• Lacks built-in support for multiple data types and complex data structures
• No native cross-platform mobile support

Code Comparison

LevelDB (C++):

leveldb::DB* db;
leveldb::Options options;
options.create_if_missing = true;
leveldb::Status status = leveldb::DB::Open(options, "/tmp/testdb", &db);
db->Put(leveldb::WriteOptions(), "key", "value");

Realm Core (C++):

auto realm = Realm::get_shared_realm(config);
realm->begin_transaction();
auto dog = realm->create<Dog>("Rex", 5);
realm->commit_transaction();

LevelDB focuses on simple key-value operations, while Realm Core provides an object-oriented database model with support for complex data structures and relationships. Realm Core offers better cross-platform support and more advanced querying capabilities, making it suitable for mobile app development. However, LevelDB's simplicity and performance make it a good choice for projects requiring basic key-value storage with high write throughput.

Pros of RocksDB

• Highly optimized for fast storage devices like SSDs
• Offers advanced features like column families and transactions
• Widely adopted and battle-tested in large-scale production environments

Cons of RocksDB

• Steeper learning curve and more complex API compared to Realm Core
• Higher memory usage, especially for large datasets
• May require more fine-tuning for optimal performance

Code Comparison

RocksDB example:

#include <rocksdb/db.h>

rocksdb::DB* db;
rocksdb::Options options;
options.create_if_missing = true;
rocksdb::Status status = rocksdb::DB::Open(options, "/path/to/db", &db);

Realm Core example:

#include <realm/db.hpp>

auto db = realm::DB::create(realm::DBOptions(realm::Realm::Config::in_memory()));
auto group = db->start_read();
auto table = group->get_table("MyTable");

Both libraries provide C++ APIs for database operations, but RocksDB's interface is more low-level and requires explicit management of database options and status checking. Realm Core offers a higher-level abstraction with a more intuitive API for common database operations.

Pros of CouchDB

• Mature and battle-tested database with a large community and extensive documentation
• Built-in support for multi-master replication and conflict resolution
• RESTful HTTP API for easy integration with web applications

Cons of CouchDB

• Slower performance for certain types of queries compared to Realm Core
• Larger resource footprint and more complex setup process
• Less suitable for mobile and embedded applications

Code Comparison

CouchDB (JavaScript):

const nano = require('nano')('http://localhost:5984');
const db = nano.use('mydb');

db.insert({ name: 'John Doe' }, 'john', (err, body) => {
  if (!err) console.log('Document inserted');
});

Realm Core (C++):

#include <realm.hpp>

realm::DB db = realm::DB::create("mydb");
realm::Table& table = db.add_table("person");
size_t name_col = table.add_column(realm::type_String, "name");
table.create_object().set(name_col, "John Doe");

Both databases offer different approaches to data storage and retrieval. CouchDB uses a document-oriented model with JSON, while Realm Core provides a more traditional object-oriented database structure. The choice between them depends on specific project requirements, such as scalability needs, performance expectations, and target platforms.

Pros of mongo

• More mature and widely adopted database system
• Extensive documentation and large community support
• Flexible schema design for complex data structures

Cons of mongo

• Higher resource consumption and slower performance for certain operations
• Steeper learning curve for advanced features and optimization
• Less suitable for mobile and edge computing scenarios

Code Comparison

mongo:

const client = new MongoClient(uri);
await client.connect();
const collection = client.db("mydb").collection("users");
await collection.insertOne({ name: "John", age: 30 });
const result = await collection.find({ age: { $gt: 25 } }).toArray();

realm-core:

auto realm = Realm::open(config);
auto users = realm->read_group().get_table("users");
users->create_object().set_all("John", 30);
auto query = users->where().greater("age", 25);
auto results = query.find_all();

Summary

While mongo offers a robust, flexible solution for server-side databases with extensive community support, realm-core provides a lightweight, efficient option better suited for mobile and edge computing. The choice between them depends on specific project requirements, such as scalability needs, performance constraints, and target platforms.