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Mingyi World Elasticsearch
Mingyi World Elasticsearch
Dec 5, 2024 · Databases

Key Elasticsearch Performance Tweaks: Cutting Query Latency from 50 ms to Under 1 ms

In a micro‑service that uses Elasticsearch to fetch product listings, a series of targeted optimizations—including shard reduction, segment merging, keyword mapping, request‑cache activation, and PIT‑based sorting—slashed query latency from 50‑60 ms to under 1 ms and boosted throughput to about 50 k queries per second.

ElasticsearchKeyword MappingPIT
0 likes · 11 min read
Key Elasticsearch Performance Tweaks: Cutting Query Latency from 50 ms to Under 1 ms
21CTO
21CTO
Oct 26, 2021 · Databases

How ElasticSearch Delivers Near Real-Time Search with Immutable Indexes

ElasticSearch achieves near real-time search by building immutable inverted indexes (segments), using incremental indexing, logical deletions, background segment merging, and a write-ahead translog to ensure durability, while distributing shards across nodes to balance load and maintain data consistency.

Near Real-Time SearchSegment Merginginverted index
0 likes · 8 min read
How ElasticSearch Delivers Near Real-Time Search with Immutable Indexes
21CTO
21CTO
Oct 9, 2021 · Backend Development

ElasticSearch Near Real-Time Search: Immutable Indexes, Segments, and Translog

This article explores how ElasticSearch delivers near real‑time search by leveraging immutable inverted indexes, segment merging, shard distribution, and a write‑ahead translog, detailing the challenges of persistence, disk I/O, and data loss prevention in a distributed environment.

Distributed SystemsNear Real-Time SearchSegment Merging
0 likes · 9 min read
ElasticSearch Near Real-Time Search: Immutable Indexes, Segments, and Translog
58 Tech
58 Tech
Mar 8, 2021 · Fundamentals

Real‑Time Inverted Index Update Techniques in the 58 Search Engine

This article explains how the 58 search engine achieves millisecond‑level real‑time inverted‑index updates by redesigning the underlying data structures, combining static and dynamic indexing, using a chain‑array hybrid, segment merging strategies, and lock‑free read‑write concurrency while maintaining search performance.

Data StructuresReal-Time UpdateSegment Merging
0 likes · 18 min read
Real‑Time Inverted Index Update Techniques in the 58 Search Engine
DevOps Coach
DevOps Coach
Oct 19, 2020 · Backend Development

Understanding Elasticsearch Segment Merging: When and How to Use Force Merge

This article explains what Elasticsearch segments are, why they are immutable, how segment merging works, its impact on resources and search performance, and provides practical configuration tips such as using force_merge, refresh_interval adjustments, and thread count settings.

ElasticsearchForce MergeIndex Management
0 likes · 9 min read
Understanding Elasticsearch Segment Merging: When and How to Use Force Merge
Beike Product & Technology
Beike Product & Technology
Nov 23, 2018 · Backend Development

Elasticsearch Internals: Distributed Document Storage, Real‑time Search, and Translog Mechanics

This article explains the core Elasticsearch architecture—including shard routing, primary‑replica interaction, document CRUD workflows, multi‑document APIs, segment merging, translog durability, and storage file formats—providing a comprehensive view of how near‑real‑time search is achieved on large‑scale data.

ElasticsearchSegment Mergingdistributed storage
0 likes · 20 min read
Elasticsearch Internals: Distributed Document Storage, Real‑time Search, and Translog Mechanics
vivo Internet Technology
vivo Internet Technology
Oct 14, 2017 · Databases

Elasticsearch Index Performance Optimization (Part 2)

To maximize Elasticsearch bulk-indexing speed, temporarily disable refreshes and replicas, tune merge throttling and scheduler threads, enlarge translog and index buffer thresholds, and adjust indexing and bulk thread-pool sizes, then restore defaults after the load completes.

ElasticsearchRefresh IntervalSegment Merging
0 likes · 13 min read
Elasticsearch Index Performance Optimization (Part 2)