Distributed kdb+ Systems

Introduction

As data volumes grow exponentially, the need for distributed systems becomes increasingly critical. Kdb+ offers robust capabilities for distributing data and computations across multiple machines. This chapter explores the concepts and techniques involved in building distributed kdb+ systems.

Clustering

A kdb+ cluster consists of multiple machines (nodes) working together as a single system.

Code snippet

// On node1:
hopen`:localhost:5001

// On node2:
hopen`:localhost:5002

// Create a cluster:
cluster:hopen`:cluster
cluster add`:localhost:5001
cluster add`:localhost:5002

Data Partitioning

To distribute data effectively, it's essential to partition it across nodes. Kdb+ offers various partitioning strategies:

• Hash partitioning: Distribute data based on a hash of a key column.

• Range partitioning: Distribute data based on a range of values in a key column.

• List partitioning: Distribute data based on a predefined list of values.

Code snippet

// Example of hash partitioning
trades:([]sym:symbol;time:`times$;price:float;size:int)
partition trades by sym

Query Distribution

Queries are distributed across cluster nodes based on data location. Kdb+ automatically handles query routing.

Code snippet

// Query is distributed to relevant nodes
select avg price from trades where sym=`AAPL

Fault Tolerance

Distributed systems must be resilient to failures. Kdb+ offers mechanisms for fault tolerance:

• Replication: Duplicate data across multiple nodes.

• Automatic failover: Automatically switch to a backup node in case of failures.

Distributed Joins

Joining data across multiple nodes can be complex. Kdb+ provides tools to handle distributed joins efficiently:

Code snippet

// Distributed join using co-locate join
trades1:([]sym:symbol;time:`times$;price:float;size:int)
trades2:([]sym:symbol;time:`times$;volume:int)

join trades1 trades2 by sym

Distributed Aggregations

Aggregations can be performed across multiple nodes. Kdb+ supports distributed aggregations for efficient calculations.

Code snippet

// Distributed sum
sum price from trades

Advanced Topics

• Distributed transactions: Ensure data consistency across multiple nodes.

• Load balancing: Distribute workload evenly across cluster nodes.

• Performance optimization: Tune cluster configuration for optimal performance.

• Security: Protect data and access to the cluster.

Conclusion

Building distributed kdb+ systems requires careful planning and consideration of various factors. By understanding the core concepts and techniques, you can create scalable and reliable applications to handle massive datasets.