Elastic Dev

👋 Welcome to Elastic Dev — your new go-to for hands-on demos, cheat sheets, concept explainers and more. Get the latest on search, data, and GenAI straight from the source. Expect: - Practical demos and how-tos - Posts and technical deep dives from our Labs - Open source updates & more For anyone who loves building, learning and experimenting: let's get started. Hello, world.

Discussion: go.es.io/3P83YJn

Too_many_buckets_exception is one of those errors that looks scary but usually means your query needs work, not a bigger limit. Common causes: • High-cardinality terms aggregations • Date histograms with too-small intervals • Deep nested aggregations • Wide dashboard time ranges Fix the query first. Raising search.max_buckets raises memory pressure. It should be a last step, not a first instinct.

Full breakdown here — covers episodic vs semantic vs procedural memory, how document-level security handles memory isolation, and a working Python notebook to build it yourself: go.es.io/4sKDc8i

Most AI agents don't have a memory problem. They have a memory management problem. Dumping full chat history into every prompt leads to: - Context poisoning - Slower responses - Higher token costs - Confused reasoning Selective memory fixes this. Here's how to build it with Elasticsearch.

One prompt. Raw data in. Live Kibana dashboard out.

Quick mental model: - LZ4 → faster stored field reads - ZSTD → better compression ratio This mostly affects stored fields and _source, not the query execution itself. One important detail: the codec only changes after segment merges. POST logs-index/_forcemerge?max_num_segments=1 So if your workload is logs, metrics, or analytics, switching from LZ4 → ZSTD can be a practical way to reduce storage while keeping indexing behavior largely unchanged.

Elasticsearch uses two compression algorithms for stored data By default it uses LZ4 for optimized fast reads. Switch to ZSTD to prioritize smaller index sizes: - Use index.codec: best_compression - Balance storage costs against decompression speed

@_jphwang @elastic 🔥

My first video for @elastic_devs is an exciting one - we've just launched @elastic Agent Skills! Try it out and your agent would already be way smarter at using Elastic than dummies like me. 😆

Read the Blog: go.es.io/4sT6XE1

Turn your AI agent into an Elastic expert. Elastic Agent Skills puts native platform expertise in your agent, built by the engineers who develop and ship Elastic. - Accurate ES|QL. - Correct Kibana dashboards. - Security and observability workflows that run correctly, first time.

Documentation: go.es.io/4baNGrK Image Source: go.es.io/3Pip9rY

Does LogsDB compression cause data loss? Short answer: no. LogsDB uses lossless compression (ZSTD / best_compression) and log-aware indexing. Your documents stay fully queryable. Synthetic _source reconstructs the original JSON at read time instead of storing it — nothing is dropped, just recalculated.

@openclaw Full tutorial + code on Elasticsearch Labs: go.es.io/4urzYrL

SearchClaw gives your local AI agent access to Elasticsearch data. Built on @OpenClaw + Elasticsearch, no custom code required. What it does: - Semantic search via semantic_text - Filtered queries and aggregations - Log observability across multiple indices Skills compose at runtime — query your products against live weather data in a single request. Read-only by design.

Running a logs-heavy workload and wondering how to cut disk usage? Synthetic _source skips storing the full JSON document. Instead, it reconstructs _source at read time, which can meaningfully reduce your storage footprint without changing how you query your data.

Day in the life of an Embedding Model @JinaAi_

Documentation: - Logs data stream (LogsDB index mode): go.es.io/3NwXiUr - Elasticsearch logsdb index mode: go.es.io/4ui4Mex - Index modules (index.mode): go.es.io/4rsNNUq

Logs are not generic documents. Treating them that way is expensive. In ELK, LogsDB uses log-aware sorting, better compression, and optimized storage layouts. Synthetic _source can reduce footprint even further. There is no data loss.

@helloiamleonie See how to give your agents the right context: go.es.io/4umkEge

Wrong index. Bad query. Garbage context. Fix it. Bad tool design means wrong indexes, broken ES|QL, and bloated context. Learn from @helloiamleonie 1. Build "low floor, high ceiling" tools for Elasticsearch 2. Use ES|QL for precise, efficient data filtering 3. Evaluate and iterate for real-world accuracy