How to deliver data at large scale

Contents

• Introduction
• 1. How to Scale Message Consumption
• 2. Partitioning in Real-Life Systems
• 3. Partitioning Strategies
• 4. Request Routing
• 5. Rebalancing Partitions
• 6. Consistent Hashing

Introduction

In today’s digital landscape, the ability to deliver data at large scale is crucial for many applications, from social media platforms to financial services. This guide delves into the various strategies and techniques used to manage and process large volumes of data efficiently. By exploring concepts such as message consumption, partitioning, request routing, rebalancing partitions, and consistent hashing, we aim to provide a comprehensive understanding of how to maintain scalability, performance, and reliability in software systems. Whether you’re dealing with a high volume of messages or vast amounts of data, these strategies will help ensure your system can handle the load effectively.

1. How to Scale Message Consumption

Imagine you have a lot of mail (messages) coming to your house, and you need to read them all. Message consumption is like picking up each letter, reading it, and doing what it says. In software, message consumption means taking messages from a queue and processing them.

Message consumption is like handling a lot of tasks or messages coming in. For example:

• If you have one person (single consumer) reading all the mail, it might take a long time.
• If you have many people (multiple consumers) reading the mail, it can be done faster, but there can be issues to manage.

Single Consumer vs Multiple Consumers:

Single Server/Consumer	Multiple Servers/Consumers
One server (or process) handles all the messages.	Many servers (or processes) handle the messages.
Easier to manage and keep track of the order.	More complex to manage, with potential issues like message order and double processing.
Slower because only one server is doing all the work.	Faster because the work is shared.

Problems with Multiple Consumers:

• Order of Message Processing:

  • Messages might arrive in a specific order, but with multiple consumers, they might get processed out of order.
  • For example, if message 1 needs to be processed before message 2, multiple consumers might mix up this order.

• Double Processing:

  • Sometimes, a message might get picked up by two consumers at the same time, leading to it being processed twice.
  • This can cause issues, especially if the message should only be processed once (like a bank transaction).

How to Handle Multiple Consumers:

• Ensure Order: Use techniques like message sequencing or ordering guarantees to ensure messages are processed in the right order.

• Prevent Double Processing:

  • Implement mechanisms like locks or acknowledgments to ensure that once a message is picked up, it isn’t processed by another consumer.
  • Use idempotent processing, where processing the same message multiple times doesn’t cause any issues.

Scaling Message Consumption:

• Horizontal Scaling: Add more consumers to handle more messages at the same time.

• Partitioning: Divide the messages into partitions, where each consumer handles a specific partition. This helps manage order and distribution better.

• Load Balancing: Distribute the messages evenly among consumers to ensure no single consumer is overloaded.

Scaling message consumption is like having more people to read your mail faster. Single consumers are simple but slow, while multiple consumers are faster but more complex. Issues like message order and double processing need careful handling. Techniques like ensuring order, preventing double processing, horizontal scaling, partitioning, and load balancing help manage these issues and scale efficiently.

2. Partitioning in Real-Life Systems

Imagine you have a big garden, and you want to grow different types of plants. Instead of mixing all the plants together, you divide the garden into sections (partitions) and grow a specific type of plant in each section. In software systems, partitioning means dividing data or tasks into smaller, manageable pieces that can be processed independently.

Partitioning is like splitting a big job into smaller parts so each part can be handled separately. For example:

• In a library, books are divided into sections based on their genre (fiction, non-fiction, science, etc.).
• In a classroom, students might be divided into groups for different activities.

In software, partitioning helps to manage large amounts of data or tasks by dividing them into smaller parts that can be processed independently.

Pros of Partitioning:

• Scalability: Makes it easier to scale the system by adding more partitions.
• Performance: Improves performance by allowing parallel processing of partitions.
• Availability: Increases availability because if one partition fails, others can still function.

Cons of Partitioning:

• Complexity: Adds complexity in managing and maintaining partitions.
• Data Distribution: Ensuring data is evenly distributed across partitions can be challenging.
• Consistency: Maintaining consistency across partitions can be difficult, especially in distributed systems.

Applications of Partitioning:

• Databases:

  • Example: SQL databases, MongoDB, Dynamo, Cassandra.
  • Usage: Data is divided into shards, each handled by different servers (nodes) to balance the load and improve performance.
  • Considerations: Deciding which shard to write data to, how to pick a node when reading data, and ensuring even distribution of data and requests.

• Messaging Systems:

  • Example: RabbitMQ, Kafka, Kinesis.
  • Usage: Messages are divided into sharded queues, allowing multiple consumers to process messages in parallel.
  • Considerations: Handling message order, preventing double processing, and ensuring efficient load distribution.

• Object Storages:

  • Example: Distributed storage systems.
  • Usage: Data files are divided and stored across multiple storage nodes, improving access speed and reliability.
  • Considerations: Assigning storage nodes, managing metadata, and ensuring data availability and retrieval efficiency.

Partitioning in real-life systems is like dividing a big task into smaller, manageable parts. It helps in scalability, performance, and availability but also adds complexity and challenges in data distribution and consistency. Applications of partitioning include databases, messaging systems, and object storages, each with specific considerations for effective management.

3. Partitioning Strategies

Imagine you have a big pile of candy, and you want to share it with your friends. You need a way to divide the candy fairly. Partitioning strategies are like different methods for dividing the candy so everyone gets a fair share. In software, partitioning strategies are methods used to divide data or tasks into smaller parts (partitions) for better management and performance.

Partitioning strategies are like different ways to split up a big job into smaller parts. For example:

• If you divide candies by color, each friend gets a specific color.
• If you divide candies by size, each friend gets a specific size range.
• If you divide candies randomly, each friend gets a random mix.

In software, partitioning strategies help in organizing and managing large amounts of data or tasks efficiently.

Lookup Strategy:

• This strategy uses a predefined table or service to determine which partition a piece of data belongs to.
• Think of it like a map that tells you where each type of candy is stored.

Pros:

• Simple and flexible as the lookup table can be easily modified.
• Can handle complex partitioning logic.

Cons:

• The lookup table can become a bottleneck if it grows too large.

• Requires additional management to keep the table updated.

        graph LR
        A[Data Item] --> B[Lookup Table]
        B --> C[Partition 1]
        B --> D[Partition 2]
        B --> E[Partition 3]
  

Range Strategy:

• Data is divided into ranges, and each range is assigned to a different partition.

• Imagine dividing candies based on their size range, small, medium, and large.

• Pros:

  • Easy to understand and implement.
  • Efficient for queries that fall within a specific range.

• Cons:

  • Can lead to uneven distribution if the data is not uniformly distributed.

  • May require rebalancing if ranges become skewed.

         graph LR
         A[Data Item] --> B[Range Check]
         B --> C[Partition 1]
         B --> D[Partition 2]
         B --> E[Partition 3]
    

Hash Strategy:

• A hash function is used to compute a hash value for each piece of data, which determines the partition.

• It’s like using a randomizer to decide which friend gets each candy.

• Pros:

  • Provides an even distribution of data across partitions.
  • Scales well with large amounts of data.

• Cons:

  • Can be complex to implement and manage.

  • Difficult to handle range queries efficiently.

         graph LR
         A[Data Item] --> B[Hash Function]
         B --> C[Partition 1]
         B --> D[Partition 2]
         B --> E[Partition 3]
    

Partitioning strategies are methods used to divide data or tasks into smaller, manageable parts. The lookup strategy uses a predefined table for partitioning, the range strategy divides data into ranges, and the hash strategy uses a hash function for even distribution. Each strategy has its pros and cons, and the choice depends on the specific requirements and characteristics of the data or tasks.

4. Request Routing

Imagine you have a big restaurant with many tables, and you need to direct customers to the right table. Request routing in software is similar; it’s about directing requests (like customers) to the right server or partition (like tables) to handle them.

Request routing is like making sure everyone gets to the right place. For example:

• In a restaurant, a host guides you to your table based on your reservation.
• In a library, a librarian directs you to the right section based on the book you’re looking for.

In software, request routing makes sure each request goes to the right server or partition for processing.

Physical and Virtual Shards:

• Physical Shards:

  • These are actual, physical servers or storage units where data is stored.
  • Think of them as real tables in a restaurant where customers can sit.

• Virtual Shards:

  • These are logical partitions within a physical shard, which can be moved or reallocated without changing the physical layout.
  • Think of them as assigned seats within the tables that can be rearranged easily.

• Pros and Cons:

  • Physical Shards: Easy to understand and manage, but harder to scale and rebalance.
  • Virtual Shards: More flexible and scalable, but add complexity in management.

Request Routing Options:

• Static Routing:

  • Requests are routed based on predefined rules.
  • Example: Always send requests for a specific user to a designated server.
  • Pros: Simple and predictable.
  • Cons: Not flexible, can’t handle changes in load dynamically.

• Dynamic Routing:

  • Requests are routed based on real-time conditions.
  • Example: Send requests to the server with the least load.
  • Pros: Flexible and can adapt to changes in load.
  • Cons: More complex to implement and manage.

• Consistent Hashing:

  • A special type of dynamic routing where a hash function determines the route.
  • Example: Hash the user ID to decide which server handles the request.
  • Pros: Even distribution of requests and easy to add/remove servers.
  • Cons: Can be complex to set up and manage.

• Load Balancing:

  • Distributes incoming requests evenly across multiple servers.
  • Example: Round-robin, where each request is sent to the next server in line.
  • Pros: Ensures no single server is overloaded.
  • Cons: Can become a bottleneck if not managed properly.

Request routing in software ensures that each request is directed to the right server or partition for processing. Physical shards are actual servers, while virtual shards are logical partitions within them. Different routing options include static routing, dynamic routing, consistent hashing, and load balancing, each with its own pros and cons. The choice of routing strategy depends on the system’s requirements and goals.

5. Rebalancing Partitions

Imagine you have a classroom with many students, and you want to make sure each group has the same number of students. If some groups have too many students while others have too few, you move students around to balance the groups. In software, rebalancing partitions means adjusting the distribution of data or tasks across different servers to ensure an even load.

Rebalancing partitions is like making sure everyone has an equal amount of work. For example:

• If one server has too much data, some data is moved to another server with less data.
• This helps in keeping things fair and efficient.

Why Rebalance Partitions?

• Uneven Data Distribution: When some partitions have much more data than others.
• Hot Keys: When certain data is accessed more frequently, causing uneven load.
• Server Failures: When a server fails, its data needs to be redistributed to other servers.

How to Rebalance Partitions:

• Splitting a Shard:

  • When a shard (partition) grows too big, it can be split into smaller shards.
  • Example: A 128 MB shard can be split into two 64 MB shards.
  • This is mainly a metadata change, but it might involve moving data if new servers are added.

• Shard Migration:

  • A process that moves data from one server to another to balance the load.
  • Triggered by adding or removing servers in the cluster.
  • Managed by a background process called the balancer.

• Merging Shards: When a shard becomes too small, it can be merged with an adjacent shard to optimize storage.

Rebalancing Strategies:

• Option 1: Split a Shard When It Grows Too Big

  • This involves splitting a large shard into smaller ones.
  • Used in systems like MongoDB and HBase.

• Option 2: Split Shards When Adding a New Server

  • When a new server is added, existing shards are split, and some are moved to the new server.
  • Used in systems like Cassandra.

• Option 3: Fixed Number of Shards

  • The system maintains a fixed number of shards regardless of the data size.
  • Easier to manage but requires careful planning of the initial number of shards.
  • Used in systems like Couchbase and Elasticsearch.

Rebalancing partitions ensures an even distribution of data and tasks across servers to maintain efficiency and performance. Techniques include splitting large shards, migrating shards when servers are added or removed, and maintaining a fixed number of shards. Each strategy has its advantages and challenges, and the choice depends on the specific requirements of the system.

Rebalancing Strategy	Partitioning Strategy	Real-world Examples
split a shard when it grows beyond the specified size	range, hash	MongoDB, HBase
split shards when adding a new server	hash	Cassandra
fixed number of shards	hash, lookup	Couchbase, Elasticsearch

6. Consistent Hashing

Imagine you have a circular table, and you want to distribute candies evenly among your friends seated around the table. Consistent hashing is like placing the candies at equal distances around the table so that each friend gets a fair share. In software, consistent hashing helps distribute data across servers in a balanced way, even when servers are added or removed.

Consistent hashing is a way to distribute data (like candies) evenly across multiple servers (friends) in a way that handles changes smoothly. For example:

• When a new friend joins or leaves the table, the candies are redistributed evenly without too much disruption.

How to Implement Consistent Hashing:

• Hash Function:

  • Use a hash function to assign each data item a position on a circular hash space (like a ring).
  • Example: Hash(data) % 360 (for a circular space of 360 degrees).

• Server Placement:

  • Each server is also assigned a position on the circular hash space using the same hash function.
  • Example: Hash(server1) % 360.

• Data Assignment:

  • Each data item is assigned to the nearest server in the clockwise direction on the hash ring.
  • Example: Data with hash value 120 is assigned to the server with hash value closest to 120 in the clockwise direction.

• Rebalancing: When a server is added or removed, only a small portion of the data needs to be reassigned, minimizing disruption.

Advantages and Disadvantages of Consistent Hashing:

• Advantages:

  • Scalability: Easily handles adding or removing servers with minimal data movement.
  • Even Distribution: Distributes data evenly across servers.
  • Fault Tolerance: If a server fails, only the data on that server needs to be redistributed.

• Disadvantages:

  • Complexity: More complex to implement compared to simple hashing.
  • Hash Collisions: Can still suffer from hash collisions if not managed properly.

Virtual Nodes:

• Virtual nodes (or vnodes) are additional positions on the hash ring for each physical server.

• Example: Each server can have multiple virtual nodes spread across the ring.

• Advantages of Virtual Nodes:

  • Improved Load Balancing: Helps in distributing data more evenly.
  • Flexibility: Makes it easier to adjust the load distribution by changing the number of virtual nodes per server.

Applications of Consistent Hashing:

• Distributed Databases: Used in systems like Cassandra and DynamoDB to distribute data across nodes.
• Caching Systems: Used in caching solutions like Memcached and Redis to distribute cached data.
• Load Balancing: Helps in distributing requests evenly across multiple servers in load balancing solutions.

Consistent hashing is a method to distribute data evenly across servers, handling changes smoothly with minimal disruption. It uses a circular hash space and assigns data to the nearest server in the clockwise direction. Virtual nodes improve load balancing and flexibility. Applications include distributed databases, caching systems, and load balancing, with advantages like scalability and fault tolerance, but also some complexity in implementation.

	Simple Hashing	Consistent Hashing
Method	Uses a hash function to map data to a fixed number of buckets (servers).	Uses a hash function to map data and servers to positions on a circular hash space (hash ring).
Data Distribution	Even distribution only if the number of buckets is fixed and data is uniform.	Even distribution across servers; minimal data reassignment when servers change.
Server Changes	Adding/removing a server requires rehashing almost all data.	Only a small portion of data needs reassignment when servers change.
Advantages	Simple and easy to implement; works well with a fixed number of servers.	Highly scalable and fault-tolerant; minimal data movement with server changes.
Disadvantages	Inflexible and not suitable for frequently changing server numbers.	More complex to implement; may require virtual nodes for even load distribution.