ELASTIC $ESTC Q1’27 EARNINGS HIGHLIGHTS 🔹 Revenue: $478M (Est. $470M) 🟢; +15% YoY 🔹 Adj. EPS: $0.70 (Est. $0.58) 🟢; +17% YoY 🔹 Current RPO: $1.15B; +21% YoY 🔹 Adjusted FCF: $143M FY27 Guide: 🔹 Revenue: $2.00B-$2.01B (Est. $1.99B) 🟢 🔹 Adj. EPS: $3.29-$3.37 (Est. $3.24) 🟢 🔹 Sales-led Subscription: $1.68B-$1.69B; +17.4% YoY 🔹 Non-GAAP Operating Margin: 19.4% 🔹 Adjusted FCF Margin: 21.5% Q2 Guide: 🔹 Revenue: $486M-$487M (Est. $483M) 🟢 🔹 Adj. EPS: $0.80-$0.82 (Est. $0.80) 🟡 🔹 Sales-led Subscription: $407.5M-$408.5M; +16.9% YoY 🔹 Non-GAAP Operating Margin: 19.0% 🔹 GAAP Operating Margin: Positive Segment Net Revenue: 🔹 Elastic Cloud: $235.2M; +20% YoY 🔹 Sales-led Subscription: $398.5M; +18% YoY 🔹 Total Subscription: $449M; +15% YoY Other Q1 Metrics: 🔹 Net Expansion Rate: 111% 🔹 Non-GAAP Operating Income: $77M Business Highlights: 🔹 Acquired Deductive AI, an AI-powered investigation platform that helps engineering teams identify and resolve production issues faster 🔹 Announced collaboration with OpenAI to bring advanced reasoning models with governed enterprise context in Elasticsearch Comments: 🔸 “beating our guidance across all key metrics” 🔸 “Our record quarter-over-quarter net customer additions to our >$100K ACV cohort and continued strength in cRPO and RPO growth reflect the durability of that demand.”

# Elasticsearch Features and Practical Usage 🔎 Write search, transform, and aggregate in a single pipe. SQL-like and easy to learn, ES|QL is Elastic's next-generation query language. 🏷️ Title: ES|QL (Piped Query Language) 🔗 URL: 📘 Overview ES|QL is Elastic's query language for querying, aggregating, visualizing, and even alerting on your data end to end. Like Unix pipes, you chain commands with `|` to progressively filter, transform, and aggregate data. You can run ad hoc analysis without writing the verbose JSON aggregation DSL. ⚙️ How It Works Every query starts with a source command, then chains processing commands via pipes. ・`FROM` selects indices/data streams (there is also `TS` for time series) ・`WHERE` filters rows ・`STATS ... BY` aggregates with grouping ・`EVAL` creates derived columns, `SORT` orders, `LIMIT` caps rows ・`KEEP`/`DROP`/`RENAME` control output columns ・`DISSECT`/`GROK` parse unstructured text ・`LOOKUP JOIN` joins lookup data, `ENRICH` applies enrich policies Commands and functions are case-insensitive (`FROM`, `from`, and `From` are equivalent). 🛠️ Practical Usage For incident investigation, you can express search-to-aggregation in one statement: `FROM logs-* | WHERE status >= 500 | STATS count = COUNT(*) BY BUCKET(@timestamp, 5m) | SORT count DESC` The Kibana editor offers autocomplete, inline suggestions, a Prettify button for auto-formatting, and a footer showing run statistics such as documents processed. The same ES|QL works across Discover, dashboard panels, alerting rules, and Elastic Security. Query history and starred queries let you reuse your go-to investigations. 💡 Use Cases ・Ad hoc log analysis for SREs (error rates by service and time bucket) ・ES|QL visualization panels on dashboards ・Detection rules and alert conditions in security ・Operational analytics joining a service name to a team via `LOOKUP JOIN` ⚠️ Caveats ・Querying many indices without filters can produce oversized responses; use `KEEP`/`DROP` to limit columns. ・Inside Kibana, handle time zones via the `dateFormat:tz` advanced setting, not `SET time_zone`. ・Natural-language query generation requires an Enterprise license and a configured connector. ・References to unmapped fields fail by default, so be careful. #Elasticsearch# #ESQL#

# Elasticsearch Features and Practical Usage 🧩 Express "a keyword plus many filters" in a single JSON document. Query DSL and its `bool` query are the de facto standard for search backends, and how you use the filter clause decides your performance. 🏷️ Title: Query DSL (JSON query language) 🔗 URL: 📘 Overview Query DSL is a JSON-style query language used through the `_search` API. It expresses searching, filtering, and aggregations, with queries built as an abstract syntax tree of interconnected clauses. It is the de facto foundation of search backend implementations. ⚙️ How It Works Two distinctions are key. ・Clause types: standalone "leaf queries" (`match`, `term`, `range`, and so on) and "compound queries" (`bool`, `dis_max`) that wrap them. ・Context: query context asks "how well does this match?" and computes `_score`. Filter context asks a binary "does this match?", skips scoring, runs faster, and is automatically cached. The central `bool` query has four clauses: `must` (must match, scored), `should` (optional, boosts score, governed by `minimum_should_match`), `filter` (must match, unscored, cached), and `must_not` (excludes, filter context). 🛠️ Practical Usage For a job search, put the keyword query in `must` and the refinements in `filter`. ``` { "query": { "bool": { "must": [ { "multi_match": { "query": "backend engineer", "fields": ["title", "description"] } } ], "filter": [ { "term": { "location": "tokyo" } }, { "terms": { "employment_type": ["fulltime", "contract"] } }, { "range": { "salary": { "gte": 5000000 } } } ] } } } ``` The keyword should influence the score, so it goes in `must`; location, employment type, and salary need no scoring, so they go in `filter`. Filter clauses get cached, making repeated queries fast. 💡 Use Cases This pattern fits any search app with "full-text plus many structured filters", such as e-commerce, jobs, or real estate. Splitting conditions between `must` (rank by relevance) and `filter` (plain match/no-match) gives you both relevance ranking and strict narrowing in one request. ⚠️ Caveats The biggest pitfall is confusing `term` and `match`. `term` matches exactly without analysis, so using it on an analyzed `text` field usually returns zero results. Use `match` for `text`, and `term` for `keyword` and structured fields like status or dates. Always put non-scoring conditions in `filter` to benefit from caching and reduced CPU. #Elasticsearch# #QueryDSL#

# Elasticsearch Features and Practical Usage 🗣️ You want "laptop" and "notebook" to find the same products. Synonyms absorb those variations and rephrasings, and the Synonyms API lets you manage them with no reindexing. 🏷️ Title: Synonyms API / synonym token filter 🔗 URL: 📘 Overview Synonyms improve relevance by matching documents that express the same concept with different words, and they help with domain-specific vocabulary and common misspellings. Managing synonym sets as independent resources through the Synonyms API lets multiple analyzers reference them and removes the need to reindex on updates. ⚙️ How It Works There are two rule formats. ・Equivalent (bidirectional): comma-separated terms like `laptop, notebook, computer`. With `expand=true` all terms map to each other; with `expand=false` they collapse to the first term as the canonical form. ・Explicit (one-way): `i-pod, i pod => ipod` replaces the left-hand terms with the right-hand term via `=>`. Two token filters exist, `synonym` and `synonym_graph`; `synonym_graph` is recommended because it handles multi-word synonyms correctly. Applying synonyms at search time means updates require no reindexing. 🛠️ Practical Usage Create a synonym set with the Synonyms API and reference it from an `updateable: true` analyzer. ``` PUT _synonyms/my-synonym-set { "synonyms_set": [ { "id": "1", "synonyms": "laptop, notebook, computer" }, { "id": "2", "synonyms": "pc => personal computer" } ] } ``` Point a `synonym_graph` filter's `synonyms_set` at this set and wire it into your search-time analyzer (`search_analyzer`). With `updateable: true`, updating the set via the API automatically reloads the associated analyzers, so changes take effect immediately. Validate the result beforehand with the `_analyze` API. 💡 Use Cases This is ideal for an e-commerce relevance-improvement loop. Analyze search logs to find rephrasings that returned zero hits, add them to the synonym set through the Synonyms API, and they take effect instantly with no reindexing. You can continuously eliminate "searched but not found" cases. ⚠️ Caveats Always use `synonym_graph` for multi-word synonyms; the legacy `synonym` filter breaks position information. Large synonym lists consume heap, and exceeding 95% trips the circuit breaker. With `lenient=false` the index can go red, so in production prefer API-managed sets over inline definitions. #Elasticsearch# #SearchRelevance#

# Elasticsearch Features and Practical Usage 📦 No need to lock down a table schema first — the moment you throw JSON at it, your data is usable. An Elasticsearch index is a document-oriented data store built for search and scale from day one. 🏷️ Title: Index / Document-oriented data store 🔗 URL: 📘 Overview An index is the fundamental unit of storage in Elasticsearch and the level at which you interact with your data. Data is stored one record at a time as JSON "documents," and each document is a set of field key-value pairs plus system metadata such as `_index`, `_id`, and `_version`. ⚙️ How It Works ・Your actual data lives in the `_source` field, while `_index` (owning index), `_id` (unique ID), and `_version` are system-managed metadata. ・A "mapping" defines each field's type and how it is indexed and queried. You pick types like `text` (for full-text search), `keyword` (exact match and aggregations), `integer`, and `date`. ・Even without an explicit mapping, "dynamic mapping" infers types from the incoming JSON, so you can add new fields later and still index them with no schema migration. ・Internally, an index is split into "shards" distributed across nodes. Data inside a shard is written as immutable "segments," and replica shards provide redundancy and scale. ・Settings like `index.number_of_shards` (fixed at creation), `index.number_of_replicas` (adjustable later), and `index.refresh_interval` (default 1s) control index behavior. 🛠️ Practical Usage Indexing a single document is straightforward. `POST products/_doc/p-1001` `{ "name": "Wireless earbuds", "price": 8900, "stock": 120, "category": "audio" }` For large loads, the `_bulk` API batches many operations in one request. `POST products/_bulk` `{ "index": { "_id": "p-1001" } }` `{ "name": "Wireless earbuds", "price": 8900 }` `{ "index": { "_id": "p-1002" } }` `{ "name": "USB-C cable", "price": 1200 }` Adding a brand-new field later (e.g. `sustainability_score`) just works thanks to dynamic mapping. 💡 Use Cases A classic pattern is modeling an e-commerce product catalog as a `products` index, one product = one JSON document (name, price, stock, category, description). A daily batch loads hundreds of thousands of records via `_bulk`, and you can introduce new attributes without waiting on an RDB schema change. ⚠️ Caveats ・A field's type cannot be changed once set. To change a type you must reindex into a new index. ・Use a regular index for frequently updated documents; use a data stream for append-only time-series data. ・Shard count and size directly affect query speed and cluster stability. Avoid huge numbers of tiny shards and aim for sensible shard sizing. #Elasticsearch# #DataModeling#

# Elasticsearch Features and Practical Usage 🌊 The era of manually rotating ` is over. With data streams your app just keeps writing to the same name, and all the backing-index management is handled for you. 🏷️ Title: Data stream (logical stream for append-only time-series data) 🔗 URL: 📘 Overview A data stream is an abstraction layer that lets you address a set of indices, optimized for append-only time-series data, under a single name. It suits continuously flowing data like logs, events, and metrics — you read and write to one resource name without worrying about the hidden "backing indices" behind it. ⚙️ How It Works ・A data stream is made of multiple auto-generated, hidden backing indices. The naming is `.ds--<>-`, where the generation is a six-digit, zero-padded integer starting at `000001` (e.g. `.ds-logs-myapp-prod-2026.06.10-000001`). ・Only the most recent "write index" accepts new documents. You cannot write directly to older backing indices. ・Searches automatically route to all backing indices, so queries span the entire dataset. ・Every document needs an `@timestamp` field mapped as `date` or `date_nanos`. If the template omits it, a default `date` mapping is applied automatically. ・A matching index template containing a `data_stream` definition is mandatory; it holds the mappings, settings, and lifecycle policy. One template can be shared across multiple data streams. ・When an age or size threshold is hit, a "rollover" creates a new backing index and switches the write index. This is automated via ILM or data stream lifecycle. 🛠️ Practical Usage First, define an index template that includes `data_stream`. `PUT _index_template/logs-myapp-template` `{ "index_patterns": ["logs-myapp-*"], "data_stream": {}, "template": { "mappings": { "properties": { "@timestamp": { "type": "date" } } } } }` Then your app simply keeps writing to the same name. `POST logs-myapp-prod/_doc` `{ "@timestamp": "2026-06-10T09:00:00Z", "level": "INFO", "message": "started" }` 💡 Use Cases A common setup is writing application logs to a `logs-myapp-prod` data stream and letting backing-index rollover be managed automatically. The app no longer rotates date-based indices (` itself; it just POSTs to the same endpoint every time. ⚠️ Caveats ・Backing index names are an internal implementation detail. They can change during restore or shrink, so never build logic (like dates) off the names. ・It is a poor fit for frequently overwriting the same ID (last-write-wins); for that, use a regular index or an alias. ・Use dedicated APIs like `update by query` and `delete by query` for modifications, and remember you cannot write directly to anything but the write index. ・You cannot delete an index template that is in use by a data stream. #Elasticsearch# #DataStreams#

We had Jev score Elasticsearch hybrid search results and let a short Python policy do the reranking. • nDCG@10 went from 0.9351 to 0.9565 (250 Amazon Queries) • Latency: 70–500ms end-to-end • Cost: $0.042/M input tokens (outputs free) No text parsing, just structured probabilities feeding business logic.

Polkadot's elastic scaling lets one chain run on several cores at once A parachain on @Polkadot normally holds a single core, so it gets one block included per relay chain block, no matter how busy it is. Elastic scaling lets a chain take several cores and push up to three blocks into the same slot. Cores are bought with polkadot:native through coretime, either in bulk or on demand. That moves the bottleneck to the chain's own side. Block producers must run fast enough to fill the extra cores, which raises the hardware demands.

Anthropic testing the price elasticity of intelligence from frontier models and it seems they found a limit. Fable is only 11% of Anthropic’s enterprise sales. Also Opus 4.8 > Opus 5 usage.