Pros of Kafka

• Highly scalable and distributed streaming platform with high throughput
• Robust ecosystem with wide industry adoption and extensive tooling
• Supports real-time data processing and complex event streaming use cases

Cons of Kafka

• Higher complexity and steeper learning curve for setup and management
• Can be resource-intensive, especially for smaller deployments
• Less optimized for large object storage compared to Ambry

Code Comparison

Kafka producer example:

Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
Producer<String, String> producer = new KafkaProducer<>(props);

Ambry client example:

RouterConfig routerConfig = new RouterConfig(verifiableProperties);
ClusterMap clusterMap = new HelixClusterManager(clusterMapConfig, helixManagerFactory);
Router router = new NonBlockingRouterFactory(routerConfig, clusterMap).getRouter();

Both Kafka and Ambry are distributed storage systems, but they serve different purposes. Kafka excels in real-time data streaming and processing, while Ambry is designed for large object storage and retrieval. Kafka offers more flexibility for complex event streaming scenarios, whereas Ambry provides better optimization for storing and accessing large files or blobs.

Pros of Pulsar

• Supports both streaming and queuing messaging models
• Built-in multi-tenancy and geo-replication features
• Scalable architecture with separate storage and serving layers

Cons of Pulsar

• More complex setup and configuration compared to Ambry
• Higher resource requirements for deployment
• Steeper learning curve for developers new to the system

Code Comparison

Ambry (Java):

BlobId blobId = new BlobId(version, BlobIdType.NATIVE, dataCenterId, accountId, containerId, partitionId, isEncrypted, BlobIdSourceType.NATIVE);
BlobProperties blobProperties = new BlobProperties(blobSize, serviceId, accountId, containerId, false);
router.putBlob(blobId, blobProperties, userMetadata, channel, new PutBlobOptionsBuilder().build(), callback);

Pulsar (Java):

PulsarClient client = PulsarClient.builder().serviceUrl("pulsar://localhost:6650").build();
Producer<byte[]> producer = client.newProducer().topic("my-topic").create();
producer.send("Hello, Pulsar!".getBytes());

Both Ambry and Pulsar are distributed storage systems, but they serve different purposes. Ambry is primarily designed for blob storage, while Pulsar focuses on messaging and event streaming. Pulsar offers more features for real-time data processing and supports multiple messaging patterns, making it suitable for a wider range of use cases. However, this comes at the cost of increased complexity and resource requirements compared to Ambry's more focused approach to blob storage.

Pros of Hadoop

• Widely adopted and supported by a large community
• Offers a comprehensive ecosystem for big data processing
• Provides robust scalability for handling massive datasets

Cons of Hadoop

• More complex setup and configuration compared to Ambry
• Higher resource requirements for running a full Hadoop cluster
• Steeper learning curve for new users

Code Comparison

Hadoop (HDFS write operation):

Configuration conf = new Configuration();
FileSystem fs = FileSystem.get(conf);
Path file = new Path("hdfs://localhost:9000/user/hadoop/file.txt");
FSDataOutputStream out = fs.create(file);
out.writeUTF("Hello, Hadoop!");
out.close();

Ambry (BlobStore write operation):

BlobId blobId = new BlobId(partitionId, "file.txt");
ByteBuffer buffer = ByteBuffer.wrap("Hello, Ambry!".getBytes());
BlobProperties properties = new BlobProperties(buffer.capacity(), "application/text");
store.put(blobId, buffer, properties);

Both Hadoop and Ambry provide distributed storage solutions, but Hadoop offers a more comprehensive ecosystem for big data processing, while Ambry focuses on providing a simpler, more lightweight blob storage system. Hadoop's HDFS is designed for handling large files and batch processing, whereas Ambry is optimized for smaller objects and faster access times.

Pros of HBase

• Mature and widely adopted project with a large community and extensive documentation
• Supports real-time read/write access to large datasets
• Offers strong consistency and automatic sharding

Cons of HBase

• Higher complexity and steeper learning curve
• Requires more resources and maintenance overhead
• Less efficient for storing and retrieving small objects

Code Comparison

HBase example (Java):

Put put = new Put(Bytes.toBytes("row1"));
put.addColumn(Bytes.toBytes("cf"), Bytes.toBytes("qual1"), Bytes.toBytes("value1"));
table.put(put);

Ambry example (Java):

BlobProperties blobProperties = new BlobProperties(blobSize, serviceId);
Router router = new RouterFactory(verifiableProperties, clusterMap).getRouter();
router.putBlob(blobId, blobProperties, userMetadata, blobContent, callback);

Key Differences

• Ambry is designed specifically for storing and serving large binary objects (blobs), while HBase is a more general-purpose columnar database
• HBase provides stronger consistency guarantees, while Ambry focuses on high throughput and low latency for blob storage
• Ambry offers simpler deployment and management for blob storage use cases, whereas HBase provides more flexibility for complex data models

Both projects have their strengths, with HBase being more suitable for general-purpose distributed database needs and Ambry excelling in blob storage scenarios.

Pros of etcd

• Designed for distributed systems and highly available key-value storage
• Strong consistency and reliability through the Raft consensus algorithm
• Well-suited for storing configuration data and service discovery

Cons of etcd

• Limited scalability for large datasets compared to Ambry
• Not optimized for storing and retrieving large binary objects
• Higher complexity in setup and maintenance for simple use cases

Code Comparison

etcd (Go):

cli, _ := clientv3.New(clientv3.Config{Endpoints: []string{"localhost:2379"}})
defer cli.Close()
ctx, cancel := context.WithTimeout(context.Background(), time.Second)
_, err := cli.Put(ctx, "key", "value")
cancel()

Ambry (Java):

RouterConfig routerConfig = new RouterConfig(verifiableProperties);
NonBlockingRouter router = new NonBlockingRouter(routerConfig, clusterMap, notificationSystem);
BlobProperties blobProperties = new BlobProperties(blobSize, serviceId);
router.putBlob(blobId, blobProperties, userMetadata, channel, null);

Summary

etcd is a distributed key-value store focused on consistency and reliability, making it ideal for configuration management and service discovery in distributed systems. Ambry, on the other hand, is designed for large-scale blob storage and retrieval, offering better scalability for large datasets. While etcd provides strong consistency through the Raft algorithm, Ambry is optimized for high-throughput blob operations. The choice between the two depends on the specific use case and requirements of the system being developed.

Pros of TiKV

• Designed as a distributed key-value store, offering better scalability for large-scale deployments
• Supports ACID transactions across multiple keys, providing stronger consistency guarantees
• Implements the Raft consensus algorithm, enhancing fault tolerance and data reliability

Cons of TiKV

• Higher complexity due to its distributed nature, potentially requiring more resources to manage
• Steeper learning curve for developers not familiar with distributed systems
• May have higher latency for simple read/write operations compared to Ambry's simpler architecture

Code Comparison

TiKV (Rust):

let client = TiKVClient::new(vec!["127.0.0.1:2379"]).await?;
let txn = client.begin().await?;
txn.put("key".to_owned(), "value".to_owned()).await?;
txn.commit().await?;

Ambry (Java):

Router router = new RouterFactory(clusterMap, responseHandler).getRouter();
BlobId blobId = new BlobId(version, BlobId.BlobIdType.NATIVE, dataCenterId, accountId, containerId, partitionId, isEncrypted, BlobId.BlobDataType.DATACHUNK);
router.putBlob(blobId, blobProperties, userMetadata, channel, callback);