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The document summarizes the evolution of the ELK stack architecture at a company from a single cluster handling all data to three specialized clusters for logs, core data processing, and a testing environment. It also provides monitoring strategies using Elastizabbix to track cluster metrics and configure alerts in Zabbix. Key lessons learned are discussed around data modeling, indexing performance, and common query issues.
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This document summarizes load testing experiments conducted on Amazon RDS using an Oracle database. The tests aimed to evaluate RDS performance under different configurations and provide a basis for future load testing. Tests were run using m2.4xlarge and m1.xlarge instance types with varying provisioned IOPS. Key results showed that provisioned IOPS had a significant impact on throughput and latency. Higher IOPS configurations achieved thousands of transactions per second but also had periods of high latency. Lower IOPS configurations had more stable performance but lower throughput. The experiments provided insights into how different factors like instance type, IOPS provisioning, and read/write ratios influence RDS and database performance under load.
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This document discusses the system architecture for collecting, storing, and processing terabytes of data from viruses. It describes using Cassandra to store variety of data sources in a scalable way, PostgreSQL for some relational data, AWS Kinesis and Spark Streaming for streaming and processing data in real-time, and providing a REST API to access insights. The overall goal is to collect petabytes of data and gain insights through analytics.
Low Level CPU Performance Profiling Examples
Here are the slides of a recent Spark meetup. The demo output files will be uploaded to http://github.com/gluent/spark-prof
Best practices for Data warehousing with Amazon Redshift - AWS PS Summit Canb...
Get a look under the hood: Understand how to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. You’ll also hear about how the University of Technology Sydney (UTS) are using Redshift. The University of Technology Sydney will describe how utilizing Amazon Redshift enabled agility in dealing with Data Quality, a capacity to scale when required, and optimizing development processes through rapid provisioning of Data Warehouse environments.
Speaker: Ganesh Raja, Solutions Architect, Amazon Web Services with Susan Gibson, Manager, Data and Business Intelligence, UTS
Level: 300
Datadog: a Real-Time Metrics Database for One Quadrillion Points/Day
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2mAKgJi.
Ian Nowland and Joel Barciauskas talk about the challenges Datadog faces as the company has grown its real-time metrics systems that collect, process, and visualize data to the point they now handle trillions of points per day. They also talk about how the architecture has evolved, and what they are looking to in the future as they architect for a quadrillion points per day. Filmed at qconnewyork.com.
Ian Nowland is the VP Engineering Metrics and Alerting at Datadog. Joel Barciauskas currently leads Datadog's distribution metrics team, providing accurate, low latency percentile measures for customers across their infrastructure.
Prácticas recomendadas en materia de arquitectura y errores que debes evitar
Crece con confianza. Desde la implementación de un pequeño nodo de desarrollo para la búsqueda de aplicaciones hasta la gestión de una gran implementación de cientos de nodos, nuestros expertos en Elastic te contarán todo lo que necesites saber.
SUE 2018 - Migrating a 130TB Cluster from Elasticsearch 2 to 5 in 20 Hours Wi...
The talk I gave at the Snow Unix Event in Nederland about upgrading a massive production Elasticsearch cluster from a major version to another without downtime and a complete rollback plan.
Centralized log-management-with-elastic-stack
Centralized log management is implemented using the Elastic Stack including Filebeat, Logstash, Elasticsearch, and Kibana. Filebeat ships logs to Logstash which transforms and indexes the data into Elasticsearch. Logs can then be queried and visualized in Kibana. For large volumes of logs, Kafka may be used as a buffer between the shipper and indexer. Backups are performed using Elasticsearch snapshots to a shared file system or cloud storage. Logs are indexed into time-based indices and a cron job deletes old indices to control storage usage.
Cassandra To Infinity And Beyond
How Teads scale with Apache Cassandra.
Internet scale means tons of data, read heavy workload, massive data ingestion and low latency.
The French AdTech company Teads uses Cassandra massively, a reliable and performant Open Source database.
Spawning Cassandra nodes in AWS is a piece of cake with Terraform and Chef.
SF Big Analytics & SF Machine Learning Meetup: Machine Learning at the Limit ...
Machine Learning at the Limit
John Canny, UC Berkeley
How fast can machine learning and graph algorithms be? In "roofline" design, every kernel is driven toward the limits imposed by CPU, memory, network etc. This can lead to dramatic improvements: BIDMach is a toolkit for machine learning that uses rooflined design and GPUs to achieve two- to three-orders of magnitude improvements over other toolkits on single machines. These speedups are larger than have been reported for *cluster* systems (e.g. Spark/MLLib, Powergraph) running on hundreds of nodes, and BIDMach with a GPU outperforms these systems for most common machine learning tasks. For algorithms (e.g. graph algorithms) which do require cluster computing, we have developed a rooflined network primitive called "Kylix". We can show that Kylix approaches the rooline limits for sparse Allreduce, and empirically holds the record for distributed Pagerank. Beyond rooflining, we believe there are great opportunities from deep algorithm/hardware codesign. Gibbs Sampling (GS) is a very general tool for inference, but is typically much slower than alternatives. SAME (State Augmentation for Marginal Estimation) is a variation of GS which was developed for marginal parameter estimation. We show that it has high parallelism, and a fast GPU implementation. Using SAME, we developed a GS implementation of Latent Dirichlet Allocation whose running time is 100x faster than other samplers, and within 3x of the fastest symbolic methods. We are extending this approach to general graphical models, an area where there is currently a void of (practically) fast tools. It seems at least plausible that a general-purpose solution based on these techniques can closely approach the performance of custom algorithms.
Bio
John Canny is a professor in computer science at UC Berkeley. He is an ACM dissertation award winner and a Packard Fellow. He is currently a Data Science Senior Fellow in Berkeley's new Institute for Data Science and holds a INRIA (France) International Chair. Since 2002, he has been developing and deploying large-scale behavioral modeling systems. He designed and protyped production systems for Overstock.com, Yahoo, Ebay, Quantcast and Microsoft. He currently works on several applications of data mining for human learning (MOOCs and early language learning), health and well-being, and applications in the sciences.
(BDT307) Running NoSQL on Amazon EC2 | AWS re:Invent 2014
Deploying self-managed NoSQL databases on Amazon Web Services (AWS) is more straightforward than you would think. This session focuses on three popular self-managed NoSQL systems on Amazon EC2: MongoDB, Cassandra, and Couchbase. We start with an overview of each of these popular NoSQL databases, discuss their origins and characteristics, and demonstrate the use of the AWS ecosystem to deploy these NoSQL databases quickly. Later in the session, we dive deep on use cases, design patterns, and discuss creating highly-available and high-performance architectures with careful consideration for failure and recovery. Whether you're a NoSQL developer, architect, or administrator, join us for a comprehensive session on looking at three different NoSQL systems from a uniform perspective.
Machine learning at Scale with Apache Spark
This document discusses scaling machine learning using Apache Spark. It covers several key topics:
1) Parallelizing machine learning algorithms and neural networks to distribute computation across clusters. This includes data, model, and parameter server parallelism.
2) Apache Spark's Resilient Distributed Datasets (RDDs) programming model which allows distributing data and computation across a cluster in a fault-tolerant manner.
3) Examples of very large neural networks trained on clusters, such as a Google face detection model using 1,000 servers and a IBM brain-inspired chip model using 262,144 CPUs.
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Managing your Black Friday Logs - Antonio Bonuccelli - Codemotion Rome 2018
Managing your Black Friday Logs - Antonio Bonuccelli - Codemotion Rome 2018
Myths of Big Partitions (Robert Stupp, DataStax) | Cassandra Summit 2016
Myths of Big Partitions (Robert Stupp, DataStax) | Cassandra Summit 2016
Managing Security At 1M Events a Second using Elasticsearch
Managing Security At 1M Events a Second using Elasticsearch
Latest performance changes by Scylla - Project optimus / Nolimits
Latest performance changes by Scylla - Project optimus / Nolimits
Yaroslav Nedashkovsky - "Data Engineering in Information Security: how to col...
Yaroslav Nedashkovsky - "Data Engineering in Information Security: how to col...
Best practices for Data warehousing with Amazon Redshift - AWS PS Summit Canb...
Best practices for Data warehousing with Amazon Redshift - AWS PS Summit Canb...
Datadog: a Real-Time Metrics Database for One Quadrillion Points/Day
Datadog: a Real-Time Metrics Database for One Quadrillion Points/Day
Prácticas recomendadas en materia de arquitectura y errores que debes evitar
Prácticas recomendadas en materia de arquitectura y errores que debes evitar
SUE 2018 - Migrating a 130TB Cluster from Elasticsearch 2 to 5 in 20 Hours Wi...
SUE 2018 - Migrating a 130TB Cluster from Elasticsearch 2 to 5 in 20 Hours Wi...
SF Big Analytics & SF Machine Learning Meetup: Machine Learning at the Limit ...
SF Big Analytics & SF Machine Learning Meetup: Machine Learning at the Limit ...
(BDT307) Running NoSQL on Amazon EC2 | AWS re:Invent 2014
(BDT307) Running NoSQL on Amazon EC2 | AWS re:Invent 2014
More from Will Button
Build an Infra Product with AWS Fargate
Slides from the Jan 2019 Phoenix DevOps talk "AWS Fargate as an Infrastructure Product". Includes speaker notes.
DevOps for Developers
Lot's of talks focus on DevOps. This one strives to leave the attendee with small, measurable, actionable steps he or she can take to start their own DevOps journey. YMMV :)
Deploy Nodejs on Docker
The document discusses best practices for using Docker for deploying Node.js applications. It covers creating a Dockerfile and docker-compose.yml file, including setting environment variables, using a non-root user, limiting memory usage, caching layers for faster builds, and volumes for local development versus remote deployment. Automating deployments with tools like Jenkins is also mentioned. The goal is to have repeatable, optimized deployments with a single command and best practices around testing and versioning.
Effective Telepresence and Remote Collaboration
Remote workers have become a mainstay, whether dealing with outsourced staff, work from home employees, or remote offices. These slides are from the Phoenix DevOps Meetup (07/27/2016) where I spoke about tips, tools, and techniques to enable better communication across remote teams.
No More Mr. Nice Guy The MEAN Stack
The document discusses building a highly available and scalable MEAN stack environment. It covers using MongoDB, Express.js, Angular, and Node.js with fault tolerance and load balancing. Key aspects include deploying to multiple availability zones, replicating databases, adding additional servers, and monitoring performance as the system scales to handle more traffic. The goal is to build an environment that can scale efficiently on AWS to support growing application needs.
Practical MongoDB
Slide notes from Desert Code Camp 2014. This talk focuses on using the MEAN Stack to build an app that uses Facebook authentication for access, demonstrates advanced permissions for reading an authenticated user's Facebook data, and generating a data visualization using the D3.js library and custom Angular directives.
Mongo Sharding: Case Study
This document summarizes a presentation about sharding in MongoDB. It discusses what sharding is, when it is necessary to shard a MongoDB deployment, how to choose a shard key, and the process of integrating sharding into a production environment. It also provides tips on monitoring shard health and things that can be done today to prepare for future sharding needs.
Mongoose and MongoDB 101
This document provides a summary of MongoDB and Mongoose 101 presented at a Phoenix MongoDB Meetup. It introduces the presenter and his background. It then provides a high-level overview of MongoDB and compares SQL and MongoDB terminology. The remainder of the document demonstrates basic CRUD operations in MongoDB using the Mongo shell and introduces Mongoose, an ORM for MongoDB, demonstrating how to define schemas and models and perform queries and validations. It also discusses subdocuments and population features in Mongoose.
Mongo db mug_2012-02-07
The document discusses aggregating data from a video game stats dataset to generate reports. Specifically, it describes using the MongoDB aggregation framework to get a count of kills for each of three players on a specific date between certain hours. The aggregation pipeline uses $match, $project, $group and $sort operators to filter, reshape, group and sort the documents before returning the results.
More from Will Button (9)
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Enterprise Resource Planning System in Telangana
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
OpenMetadata Community Meeting - 5th June 2024
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
Presentation from Ghazal Aakel and Eric Jochum at the GSD Community Stage Meetup on June 6th, 2024
A Sighting of filterA in Typelevel Rite of Passage
Slide deck home: https://fpilluminated.com/deck/220
GOING AOT WITH GRAALVM FOR SPRING BOOT (SPRING IO)
This session covers GraalVM & Spring Boot, Spring AOT, performance optimizations and using libraries
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
原版一模一样【微信：741003700 】【美国纽约州立大学奥尔巴尼分校毕业证学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Hand Rolled Applicative User Validation Code Kata
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx
WhatsApp offers simple, reliable, and private messaging and calling services for free worldwide. With end-to-end encryption, your personal messages and calls are secure, ensuring only you and the recipient can access them. Enjoy voice and video calls to stay connected with loved ones or colleagues. Express yourself using stickers, GIFs, or by sharing moments on Status. WhatsApp Business enables global customer outreach, facilitating sales growth and relationship building through showcasing products and services. Stay connected effortlessly with group chats for planning outings with friends or staying updated on family conversations.
GraphSummit Paris - The art of the possible with Graph Technology
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Essentials of Automations: The Art of Triggers and Actions in FME
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Why Mobile App Regression Testing is Critical for Sustained Success_ A Detail...
A dynamic process unfolds in the intricate realm of software development, dedicated to crafting and sustaining products that effortlessly address user needs. Amidst vital stages like market analysis and requirement assessments, the heart of software development lies in the meticulous creation and upkeep of source code. Code alterations are inherent, challenging code quality, particularly under stringent deadlines.
Codeigniter VS Cakephp Which is Better for Web Development.pdf
When deliberating between CodeIgniter vs CakePHP for web development, consider their respective strengths and your project requirements. CodeIgniter, known for its simplicity and speed, offers a lightweight framework ideal for rapid development of small to medium-sized projects. It's praised for its straightforward configuration and extensive documentation, making it beginner-friendly. Conversely, CakePHP provides a more structured approach with built-in features like scaffolding, authentication, and ORM. It suits larger projects requiring robust security and scalability. Ultimately, the choice hinges on your project's scale, complexity, and your team's familiarity with the frameworks.
Graspan: A Big Data System for Big Code Analysis
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
What is Augmented Reality Image Tracking
Augmented Reality (AR) Image Tracking is a technology that enables AR applications to recognize and track images in the real world, overlaying digital content onto them. This enhances the user's interaction with their environment by providing additional information and interactive elements directly tied to physical images.
Cracking the code review at SpringIO 2024
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
May Marketo Masterclass, London MUG May 22 2024.pdf
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Launch Your Streaming Platforms in Minutes
The claim of launching a streaming platform in minutes might be a bit of an exaggeration, but there are services that can significantly streamline the process. Here's a breakdown:
Pros of Speedy Streaming Platform Launch Services:
No coding required: These services often use drag-and-drop interfaces or pre-built templates, eliminating the need for programming knowledge.
Faster setup: Compared to building from scratch, these platforms can get you up and running much quicker.
All-in-one solutions: Many services offer features like content management systems (CMS), video players, and monetization tools, reducing the need for multiple integrations.
Things to Consider:
Limited customization: These platforms may offer less flexibility in design and functionality compared to custom-built solutions.
Scalability: As your audience grows, you might need to upgrade to a more robust platform or encounter limitations with the "quick launch" option.
Features: Carefully evaluate which features are included and if they meet your specific needs (e.g., live streaming, subscription options).
Examples of Services for Launching Streaming Platforms:
Muvi [muvi com]
Uscreen [usencreen tv]
Alternatives to Consider:
Existing Streaming platforms: Platforms like YouTube or Twitch might be suitable for basic streaming needs, though monetization options might be limited.
Custom Development: While more time-consuming, custom development offers the most control and flexibility for your platform.
Overall, launching a streaming platform in minutes might not be entirely realistic, but these services can significantly speed up the process compared to building from scratch. Carefully consider your needs and budget when choosing the best option for you.
Mobile App Development Company In Noida | Drona Infotech
Looking for a reliable mobile app development company in Noida? Look no further than Drona Infotech. We specialize in creating customized apps for your business needs.
Visit Us For : https://www.dronainfotech.com/mobile-application-development/
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ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
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GOING AOT WITH GRAALVM FOR SPRING BOOT (SPRING IO)
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GraphSummit Paris - The art of the possible with Graph Technology
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Essentials of Automations: The Art of Triggers and Actions in FME
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Why Mobile App Regression Testing is Critical for Sustained Success_ A Detail...
Codeigniter VS Cakephp Which is Better for Web Development.pdf
Codeigniter VS Cakephp Which is Better for Web Development.pdf
May Marketo Masterclass, London MUG May 22 2024.pdf
May Marketo Masterclass, London MUG May 22 2024.pdf
Mobile App Development Company In Noida | Drona Infotech
Mobile App Development Company In Noida | Drona Infotech
Traxticsearch
- 1. Traxticsearch Search for the elusive ELK Stack
- 2. Previous Architecture One Cluster: 3 master nodes 12 data nodes Logs, Processing data, “User” data
- 3. Current Architecture Logstash Cluster: 3 master/ 9 data Logs only Custer Cluster: 3 master/ 10 data Processing data, mission critical Soon to be firewalled off Winston Cluster: 3 master/ 3 data “Prod” quality playground Kibana access requires CCB to create index/dashboard
- 4. Stats by Cluster Logstash Cluster: 12 nodes 1,251 indices 1,158 shards 436M docs 716 GB data (1,158 closed indices) Custer Cluster: 13 nodes 1,187 indices 1,995 shards 115M docs 1.75 TB data (559 closed indices) Winston Cluster: 6 nodes 2 indices 3 shards 10M docs 5.39 GB data (0 closed indices)
- 5. Decision to split Data types Data usage SLAs + Use cases Better Performance
- 6. Elastizabbix: Monitoring ● Written Angrily (...friday night) ● Old fashioned ● Auto-discovers nodes and indices ● Dot-notation syntax to collect anything ● Managed from the zabbix user interface ● Will not overload the cluster with data collection ● Works surprisingly well
- 7. Elastizabbix: Monitoring Elastic Stats API: GET _cluster/stats “indices”: { "docs": { "count": 418156163, "deleted": 2278242 } } Zabbix Item (avoids scripting): elastizabbix[cluster, indices.docs.count] = 418156163
- 8. Elastizabbix: Alerting Triggers (get an adult!): {elastizabbix[nodes,nodes.{#NODE}.jvm.mem.heap_used_percent].last()}>95 = Disaster! ● Escalate to operations (email, XMPP, slack, kibana, etc) ● Look at your favorite monitoring tool (zabbix, marvel, HQ, Kopf, etc) ● Do something about it before the API becomes unreliable.
- 9. The quest for mbeans Relying on the Elasticsearch API for monitoring/statistics is the equivalent of relying on the patient for info during surgery.
- 10. Things I wish I knew before...
- 11. get data out of jail
- 13. Use case ● time based ? ● sharding strategies
- 14. Bulk Indexing ● Tune for payload size not doc count ~ 5-15MB ● EsRejectedExecutionException or TOO_MANY_REQUESTS (429) ● Handling failures
- 15. Mapping ● _default_ mapping ● dynamic mapping ● templates
- 16. Eventually we learned... ● 0 vs 0.0 ● 253 -1 vs 263 -1 ● lucene query syntax ● bazillion shards