Solr for Analytics
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This document discusses big data and analytics. It contains quotes about big data and discusses using Solr for analytics and metrics aggregation. It describes different ways that analytics data can be manifested and stored in Solr, including using docValues. It also outlines ways that Solr could be improved to be more analytics friendly in the future.
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datamining_Lecture_1(introduction).pptx
وطن
استكشف
بحث
إرسال بحث
رفع
H
School management system
تحميل عرض تقديمي لتنزيله
نظام إدارة المدارس
إضافة المزيد من المعلومات إلى التحميل
العروض التقديمية والمستندات والرسوم البيانية والمزيد
datamining_Lecture_1(مقدمة).pptx
جاري إنشاء تحميل...
عنوان*
datamining_Lecture_1(introduction).pptx
*مطلوب
وصف*
*مطلوب
0/3000
باب*
إنترنت
*مطلوب
العلامات
تسويق
تصميم
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التقنية
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درجة قابلية الاكتشاف
تزداد درجاتك كلما اخترت فئة ، وملء وصف طويل وإضافة المزيد من العلامات.
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© 2023 SlideShare من Scribd
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datamining_Lecture_1(introduction).pptx
وطن
استكشف
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إرسال بحث
رفع
H
School management system
تحميل عرض تقديمي لتنزيله
نظام إدارة المدارس
إضافة المزيد من المعلومات إلى التحميل
العروض التقديمية والمستندات والرسوم البيانية والمزيد
datamining_Lecture_1(مقدمة).pptx
جاري إنشاء تحميل...
عنوان*
datamining_Lecture_1(introduction).pptx
*مطلوب
وصف*
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باب*
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© 2023 SlideShare من Scribd
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New Developments in Machine Learning - Prof. Dr. Max Welling
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Is one enough? Data warehousing for biomedical research
Is one enough? Data warehousing for biomedical research
Perceptive Analytics Unlock Value from Data Visualizations - Informs'14 Boston
Perceptive Analytics Unlock Value from Data Visualizations - Informs'14 Boston
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Tweaking the Base Score: Lucene/Solr Similarities Explained
This talk was given during Activate Conference 2019. Lucene has a lot of options for configuring similarity, and Solr inherits them. Similarity makes the base of your relevancy score: how similar is this document to the query? The default similarity (BM25) is a good start, but you may need to tweak it for your use-case. In this session, you will learn how BM25 works and how you may want to change its parameters. Then, we'll move to other similarity classes: DFR, DFI, IB and LM. You will learn the thinking behind them, how that thinking translates to the similarity score, and which parameters allow you to tweak how score evolves based on things like term frequency or document length. By the end, you’ll have a good understanding of which similarity options are likely to work well for your use-case. You'll know which tunables are available and whether you need to implement a custom similarity class. As an example, we’ll focus on E-commerce, where you often end up ignoring term frequency altogether.
Key Takeaway
1) What are the built-in Lucene/Solr similarities and what they do
2) Which similarity to use for which use-case
3) How to use a custom similarity class in Solr
Learn more about search relevance and similarity: sematext.com/blog/search-relevance-solr-elasticsearch-similarity
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Is observability good for your brain?
This talk was given during Monitorama EU 2018.
Observability, like other ops practices, has hard and soft benefits. No logs - no root cause, that’s a hard benefit. A soft benefit is when we have more confidence in an observable system. Then we can be more productive in developing it. The trouble with soft benefits like confidence, is how to measure them. Does observability actually make us more productive? How about other activities, such as post-mortems? Why is alert fatigue so bad? Turns out, there are plenty of studies about the impact of such activities on our brain, our behavior, our productivity. In this session, we’ll explore what [neuro]science says about such practices so that:
We turn soft benefits into hard benefits
We can encourage a culture where we get the benefits and avoid the traps
Be prepared for surprises, as some “best practices” aren’t “best” at all.
Introducing log analysis to your organization
The document discusses introducing log analysis to an organization. It covers log shipping architecture using file shippers, centralized buffers like Kafka and Redis, and storage and analysis using Elasticsearch, Kibana and Grafana. Specific topics covered include choosing the right shipper, buffer types, protocols, and optimizing Elasticsearch configuration, indices, and hardware for different node types like data, ingest and client nodes.
Solr Search Engine: Optimize Is (Not) Bad for You
This talk was given during Lucene Revolution 2017.
They say optimize is bad for you, they say you shouldn't do it, they say it will invalidate operating system caches and make your system suffer. This is all true, but is it true in all cases?
In this presentation we will look closer on what optimize or better called force merge does to your Solr search engine. You will learn what segments are, how they are built and how they are used by Lucene and Solr for searching. We will discuss real-life performance implications regarding Solr collections that have many segments on a single node and compare that to the Solr where the number of segments is moderate and low. We will see what we can do to tune the merging process to trade off indexing performance for better query performance and what pitfalls are there waiting for us. Finally, at the end of the talk we will discuss possibilities of running force merge to avoid system disruption and still benefit from query performance boost that single segment index provides.
Solr on Docker - the Good, the Bad and the Ugly
The document summarizes the good, bad, and ugly aspects of using Solr on Docker. The good is the orchestration and ability to dynamically allocate resources which can deliver on the promise of development, testing, and production environments being the same. The bad is that treating instances as cattle rather than pets requires good sizing, configuration, and scaling practices. The ugly is that the ecosystem is still young, leading to exciting bugs as Docker is still the future.
Monitoring and Log Management for
This document discusses centralized logging and monitoring for Docker Swarm and Kubernetes orchestration platforms. It covers collecting container logs and metrics through agents, automatically tagging data with metadata, and visualizing logs and metrics alongside events through centralized log management and monitoring systems. An example monitoring setup is described for a Swarm cluster of 3000+ nodes running 60,000 containers.
Introduction to solr
Radu Gheorghe gives an introduction to Solr, an open source search engine based on Apache Lucene. He discusses when Solr would be used, such as for product search, as well as when it may not be suitable, such as for sparse data. The presentation covers how Solr works with inverted indexes and scoring documents, as well as features like facets, streaming aggregations, master-slave and SolrCloud architectures. A demo is offered to illustrate Solr functionality.
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Elasticsearch for Logs & Metrics - a deep dive
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How to Run Solr on Docker and Why
Docker is all the rage these days. While one doesn't hear much about Solr on Docker, we're here to tell you not only that it can be done, but also share how it's done.
We'll quickly go over the basic Docker ideas - containers are lighter than VMs, they solve "but it worked on my laptop" issues - so we can dive into the specifics of running Solr on Docker.
We'll do a live demo showing you how to run Solr master - slave as well as SolrCloud using containers, how to manage CPU assignments, constraint memory and use Docker data volumes when running Solr in containers. We will also show you how to create your own containers with custom configurations.
Finally, we'll address one of the core Solr questions - which deployment type should I use? We will demonstrate performance differences between the following deployment types:
- Single Solr instance running on a bare metal machine
- Multiple Solr instances running on a single bare metal machine
- Solr running in containers
- Solr running on virtual machine
- Solr running on virtual machine using unikernel
For each deployment type we'll address how it impacts performance, operational flexibility and all other key pros and cons you ought to keep in mind.
Tuning Solr & Pipeline for Logs
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We'll start by looking at how to scale SolrCloud through a hybrid approach using a combination of time- and size-based indices, and also how to divide the cluster in tiers in order to handle the potentially spiky load in real-time. Then, we'll look at tuning individual nodes. We'll cover everything from commits, buffers, merge policies and doc values to OS settings like disk scheduler, SSD caching, and huge pages.
Finally, we'll take a look at the pipeline of getting the logs to Solr and how to make it fast and reliable: where should buffers live, which protocols to use, where should the heavy processing be done (like parsing unstructured data), and which tools from the ecosystem can help.
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Sematext engineer Rafal Kuc (@kucrafal) walks through the details of running high-performance, fault tolerant Elasticsearch clusters on Docker. Topics include: Containers vs. Virtual Machines, running the official Elasticsearch container, container constraints, good network practices, dealing with storage, data-only Docker volumes, scaling, time-based data, multiple tiers and tenants, indexing with and without routing, querying with and without routing, routing vs. no routing, and monitoring. Talk was delivered at DevOps Days Warsaw 2015.
Large Scale Log Analytics with Solr (from Lucene Revolution 2015)
In this talk from Lucene/Solr Revolution 2015, Solr and centralized logging experts Radu Gheorghe and Rafal Kuć cover topics like: flow in Logstash, flow in rsyslog, parsing JSON, log shipping, Solr tuning, time-based collections and tiered clusters.
From Zero to Production Hero: Log Analysis with Elasticsearch (from Velocity ...
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This talk covers the basics of centralizing logs in Elasticsearch and all the strategies that make it scale with billions of documents in production. Topics include:
- Time-based indices and index templates to efficiently slice your data
- Different node tiers to de-couple reading from writing, heavy traffic from low traffic
- Tuning various Elasticsearch and OS settings to maximize throughput and search performance
- Configuring tools such as logstash and rsyslog to maximize throughput and minimize overheadDocker Logging Webinar
Sematext's DevOps Evangelist, Stefan Thies (@seti321), takes a Docker Logging tour through the different log collection options Docker users have, the pros and cons of each, specific and existing Docker logging solutions, tooling, the role of syslog, log shipping to ELK Stack, and more. Q&A session at end.
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For the Docker users out there, Sematext's DevOps Evangelist, Stefan Thies, goes through a number of different Docker monitoring options, points out their pros and cons, and offers solutions for Docker monitoring. Webinar contains actionable content, diagrams and how-to steps.
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This document discusses Sematext's monitoring and logging products and services. It introduces Sematext, which is headquartered in Brooklyn and has employees globally. It then discusses why performance monitoring, log searching, and anomaly alerting are needed capabilities (Why). The document proceeds to describe Sematext's SPM and Logsene products, which provide these capabilities using open source technologies like OpenTSDB, Elasticsearch, and Kafka. It covers how the SPM agent collects metrics and traces and how Logsene ingests and analyzes logs at scale.
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Solr for Analytics
- 1. Hi :)
- 2. "Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it..." - Dan Ariely, Duke University
- 3. Solr for Analytics Metrics Aggregations at Sematext Otis Gospodnetić @otisg
- 4. OG - Pro … since circa 2000 …. since 2007
- 7. Bread Love
- 8. Nuts!
- 12. The Surface
- 13. Under the Hood
- 14. ?
- 15. Under the Hood
- 17. Schema <field name="id" type="string" indexed="true" stored="true" required="true" multiValued="false"/> <field name="baseRowKey" type="binary" indexed="false" stored="true" required="true" multiValued="false"/> <field name="dimListId" type="int" <field name="metricsData" type="binary" indexed="false" stored="true" required="true" multiValued="false"/> <dynamicField name="dim_str_*" /> indexed="true" stored="true" required="true" multiValued="false"/> type="string" indexed="true" stored="false" multiValued="true" docValues="true" <dynamicField name="dim_num_*" type="tlong" indexed="true" stored="false" multiValued="true" docValues="true" /> <dimensionList name="solr_caches_metrics_cube" formula="system*(month+week+day+hour+five_minutes+minute)*solr_cache_name*host*solr_core_name"/>
- 18. Append on Write; Compact Later?
- 20. github.com / sematext / HBaseHUT Ideally
- 21. Merge on Read
- 22. Merge on Read
- 24. Write Friendly Present SOLR-4816 - CloudSolrServer SOLR-5232 - Streaming updates between nodes SOLR-3249 - Javabin between nodes SOLR-???? - Aggregating URP
- 25. 4.4 vs. 4.5 4.4 - no routing vs. 4.5 routing via CloudSolrServer Faster AFS flushes Faster indexing: 3.5 M vs. 4 M docs in 55 min Lower heap: circa 10%
- 26. DocValues <field name="id" /> type="string" indexed="true" stored="true" required="true" docValues="true" <dynamicField name="dim_str_*" /> type="string" indexed="true" stored="false" multiValued="true" docValues="true" <dynamicField name="dim_num_*" type="tlong" indexed="true" stored="false" multiValued="true" docValues="true" /> Forward index data structure … in memory and/or on disk … compressible … off-heap See https://cwiki.apache.org/confluence/display/solr/DocValues http://searchhub.org/2013/04/02/fun-with-docvalues-in-solr-4-2/
- 27. !DocValues vs. DocValues With DocValues: … smaller heap … more numerous, but smaller GCs … roughly equal query latency … faster faceting, sorting, and grouping (but we are not using that in SPM with Solr) … didn’t see slow down in indexing (hmmm)
- 29. Analytics Friendly Future LUCENE-5189 - Numeric DocValues Updates SOLR-2894 - Distributed Pivot Facets SOLR-5302 - Analytics Component SOLR-4465 - Configurable Collectors SOLR-5045 - Pluggable Analytics SOLR-5027 - Field Collapsing PostFilter
- 30. FIN. Questions? sematext.com/about/jobs 20% off: LR2013SPM20 /spm /logsene /search-analytics otis@sematext.com @otisg @sematext