Elasticsearch Budapest March

🍃Spring's here, so is a new episode of Elasticsearch Budapest Meetup! 🌱

This will be an English speaking event. Thanks for the snacks and location provided by ingatlan.com.

⏰ Venue opens at 18:00, the first talk will start at 18:30.

This time, we'll have 3 amazing topics covered by 3 awesome speakers!

🎙 Index Lifecycle Management and Frozen Indexes
😊 Abdon Pijpelink
💼 Elastic

The latest release of the Elastic Stack, version 6.6, has introduced two new mechanisms for managing your Elasticsearch indexes: Index Lifecycle Management and Frozen Indexes. In this short presentation I want to introduce these two new features and show how they can be used.
Index Lifecycle Management (ILM) makes it easier to manage your time-based indexes, by defining a "policy" that defines the "phases" in the life in an index. This makes it easier to set up a hot-warm-cold architecture without the need for external tooling like custom scripts or Curator.
Frozen Indexes are similar to closed indexes, but contrary to closed indexes frozen indexes are replicated. A frozen index does not take up any heap, allowing for a much higher ratio of disk storage to heap, at the expense of search latency.

Abdon is a trainer at Elastic, based out of Amsterdam. He'll be in town for the Elasticsearch Engineer 1 and 2 trainings that Elastic is organizing in Budapest.

🎙 To a billion messages and beyond — A story about the evolution of Meltwater’s log ingestion pipeline
😊 János Csorvási
💼 Meltwater

Logs are great, huh? Well... not really. Unless you need them. But when you need them, you REALLY need them. Especially when you’re debugging and set level to TRACE, in a critical loop in your data processing pipeline, spewing out gigabytes of data in mere minutes. What happens then?

Join us to learn about how Meltwater, a leading media monitoring company, evolved its log ingestion pipeline over the years, from indexing 50 events per second to more than 100.000 events per second (2 billion events per day) today. This is a story about evolution, data pipelines, bugfixes leading to capacity problems, scalability challenges, datacenter-to-cloud migration, massive data volume increases, war stories and last but not least, how our team is preparing to ingest 100 times this.

Technologies covered include Elasticsearch, Logstash, Kibana, Redis, Kafka, AWS S3, AWS Lambda, Docker, Mesos/Marathon and Kubernetes.

János is an Infrastructure Engineer at Meltwater, the global leading media intelligence platform. He's part of a team that provides centralized services for fellow developers and works to enable them to be as efficient as possible in delivering business value, which also means he's constantly on the move to meet with other teams all over the world.

🎙 Made to Measure:
Ranking Evaluation using Elasticsearch
😊 Daniel Schneiter
💼 Elastic

The evaluation of search ranking results is a complicated task, but the new Elasticsearch Ranking Evaluation API tries to make it easier to measure well-known information retrieval metrics. This helps to make better relevance tuning decisions and allows to evaluate and optimize query templates over a wider range of user needs. There will be a short introduction into ranking evaluation followed by a demo of the API.

Daniel has been working in the search industry for more than a decade and is a trainer at Elastic.
He is based in Zurich and will be in town for the Elasticsearch Engineer 1 and 2 trainings in Budapest.

🌱🍃🌱🍃

RSVP to the event and let's meet!

If you have any questions or ideas, don't hesitate to share them!

Also: if you'd like to join for a future event as a speaker or host us as a venue, let me know!