Distributed search under the hood

How do you run fast aggregations on distributed data? See how Elasticsearch trades tiny accuracy for massive speed gains.

#1about 3 minutes

Understanding the benefits of distributed systems

Distributed systems offer advantages like load sharing, increased reliability through redundancy, and faster processing via parallelization.

#2about 5 minutes

Navigating the complexities of distributed computing

Moving from a single machine to a distributed environment introduces significant complexity in communication, coordination, and error handling, as highlighted by the fallacies of distributed computing.

#3about 3 minutes

How distributed systems achieve consensus

Consensus algorithms are crucial for maintaining a consistent state across all nodes, enabling tasks like cluster membership management, data writes, and leader election.

#4about 4 minutes

Introducing the core principles of Elasticsearch

Elasticsearch is a distributed search engine built for speed, scale, and relevance, offering resiliency and flexibility for use cases from e-commerce to observability.

#5about 5 minutes

Managing the cluster with a master node

Elasticsearch uses a master node to manage the cluster state, which includes node membership and data placement, and distributes this state to all nodes to ensure a consistent view.

#6about 3 minutes

Distributing data using shards and replicas

Data in Elasticsearch is partitioned into shards, with replica shards providing redundancy and read scalability, allowing the system to scale horizontally.

#7about 2 minutes

Understanding the two-phase distributed search process

A search request is handled in two phases, first querying all relevant shards for top results and then fetching the full documents from only the necessary shards.

#8about 1 minute

Optimizing query routing with adaptive replica selection

Instead of random routing, adaptive replica selection improves query performance by sending requests to shards on less busy nodes based on their recent response times.

#9about 3 minutes

Accelerating top-k queries with result skipping

Search performance can be dramatically improved by dynamically optimizing queries to skip documents that cannot possibly make it into the top results, at the cost of an exact total hit count.

#10about 3 minutes

Navigating the challenges of distributed aggregations

Calculating aggregations like term counts across distributed shards is complex and can lead to inaccuracies if not all data is considered, requiring careful handling of partial results.

#11about 3 minutes

Efficient aggregations with probabilistic data structures

Probabilistic data structures like HyperLogLog++ and T-Digest enable memory-efficient cardinality and percentile aggregations by trading perfect accuracy for significantly reduced resource usage.

#12about 5 minutes

Embracing trade-offs in distributed system design

Building and operating distributed systems involves accepting trade-offs between consistency, availability, and performance, making it crucial to understand the specific behaviors of your chosen system.

#13about 6 minutes