This document discusses Etsy's journey with Elasticsearch, Logstash, and Kibana (ELK) over 3 years. It covers lessons learned in monitoring and scaling Logstash and Elasticsearch clusters. Key topics include sizing Elasticsearch and Logstash clusters based on resources like CPU, memory, disk I/O, and networking. Monitoring systems and metrics is also discussed. The document provides advice on optimizing Logstash performance by measuring baselines, managing garbage collection, and writing custom plugins when needed. Testing configuration changes is emphasized.
(BDT401) Amazon Redshift Deep Dive: Tuning and Best PracticesAmazon Web Services
Get a look under the covers: Learn tuning best practices for taking advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. This session explains how to migrate from existing data warehouses, create an optimized schema, efficiently load data, use work load management, tune your queries, and use Amazon Redshift's interleaved sorting features. Finally, learn how TripAdvisor uses these best practices to give their entire organization access to analytic insights at scale.
In this presentation, we are going to discuss how elasticsearch handles the various operations like insert, update, delete. We would also cover what is an inverted index and how segment merging works.
elasticsearch의 기본적인 working에 대한 발표자료입니다.
특히나 logging보다는 '검색 서비스'에 포커싱된 자료이기 때문에 '한글검색' 으로 고통받으실 분들을 위한 기초 자료라 생각해주시면 감사하겠습니다.
맞지않는 정보와 오탈자 그리고 의문점이 든다면 dydwls121200@gmail.com으로 언제든지 가벼운 마음으로 메일주세요. 저 또한 성장시키는 일이기도 하니까요. 환영합니다.
Collabnix Community conduct webinar on regular basis. Swapnasagar Pradhan, an engineer from VISA delivered a talk on Traefik this January 11th 2020. Check this out.
Clickhouse Capacity Planning for OLAP Workloads, Mik Kocikowski of CloudFlareAltinity Ltd
Presented on December ClickHouse Meetup. Dec 3, 2019
Concrete findings and "best practices" from building a cluster sized for 150 analytic queries per second on 100TB of http logs. Topics covered: hardware, clients (http vs native), partitioning, indexing, SELECT vs INSERT performance, replication, sharding, quotas, and benchmarking.
(BDT401) Amazon Redshift Deep Dive: Tuning and Best PracticesAmazon Web Services
Get a look under the covers: Learn tuning best practices for taking advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. This session explains how to migrate from existing data warehouses, create an optimized schema, efficiently load data, use work load management, tune your queries, and use Amazon Redshift's interleaved sorting features. Finally, learn how TripAdvisor uses these best practices to give their entire organization access to analytic insights at scale.
In this presentation, we are going to discuss how elasticsearch handles the various operations like insert, update, delete. We would also cover what is an inverted index and how segment merging works.
elasticsearch의 기본적인 working에 대한 발표자료입니다.
특히나 logging보다는 '검색 서비스'에 포커싱된 자료이기 때문에 '한글검색' 으로 고통받으실 분들을 위한 기초 자료라 생각해주시면 감사하겠습니다.
맞지않는 정보와 오탈자 그리고 의문점이 든다면 dydwls121200@gmail.com으로 언제든지 가벼운 마음으로 메일주세요. 저 또한 성장시키는 일이기도 하니까요. 환영합니다.
Collabnix Community conduct webinar on regular basis. Swapnasagar Pradhan, an engineer from VISA delivered a talk on Traefik this January 11th 2020. Check this out.
Clickhouse Capacity Planning for OLAP Workloads, Mik Kocikowski of CloudFlareAltinity Ltd
Presented on December ClickHouse Meetup. Dec 3, 2019
Concrete findings and "best practices" from building a cluster sized for 150 analytic queries per second on 100TB of http logs. Topics covered: hardware, clients (http vs native), partitioning, indexing, SELECT vs INSERT performance, replication, sharding, quotas, and benchmarking.
This talk will tell the story of an analytics use case database from a non-OLAP and ACID-compliant RDBMS (MySQL) perspective.
I will cover the basics of the Clickhouse database Sample Clickhouse installation in a lab environment.
We are configuring Clickhouse for essential operations.
We will load the sample data set and monitor it.
We will query and visualize the results.
This talk will also base on how Kubernetes can help Clickhouse implementation via an operator.
Conclusions will include Do's and Don't of this emerging technology. Best practices and some advice around ingesting and analyzing terabytes of data efficiently.
Vitess VReplication: Standing on the Shoulders of a MySQL GiantMatt Lord
Vitess provides a large set of features that allow you to use and manage a scalable set of MySQL database instances across custom partitions or shards of your dataset as if it was a single logical database. One of the key components used within Vitess is called VReplication.
In this talk, we'll cover what VReplication is and how it relates to MySQL replication, including how VReplication leverages the technologies you're already familiar with while expanding on them to add a set of powerful primitives and abstractions that support an ever-growing list of high-level features such as sharding and resharding of tables, materialized views, online DDL, change streams (CDC), and message or job queues.
This talk should leave a MySQL user/operator with a good understanding of what VReplication could do for them and when they may want to use it.
ElasticSearch introduction talk. Overview of the API, functionality, use cases. What can be achieved, how to scale? What is Kibana, how it can benefit your business.
EXPLAIN ANALYZE is a new query profiling tool first released in MySQL 8.0.18. This presentation covers how this new feature works, both on the surface and on the inside, and how you can use it to better understand your queries, to improve them and make them go faster.
This presentation is for everyone who has ever had to understand why a query is executed slower than anticipated, and for everyone who wants to learn more about query plans and query execution in MySQL.
MySQL Administrator
Basic course
- MySQL 개요
- MySQL 설치 / 설정
- MySQL 아키텍처 - MySQL 스토리지 엔진
- MySQL 관리
- MySQL 백업 / 복구
- MySQL 모니터링
Advanced course
- MySQL Optimization
- MariaDB / Percona
- MySQL HA (High Availability)
- MySQL troubleshooting
네오클로바
http://neoclova.co.kr/
Anoop Sam John and Ramkrishna Vasudevan (Intel)
HBase provides an LRU based on heap cache but its size (and so the total data size that can be cached) is limited by Java’s max heap space. This talk highlights our work under HBASE-11425 to allow the HBase read path to work directly from the off-heap area.
Best Practices for Data Warehousing with Amazon Redshift | AWS Public Sector ...Amazon Web Services
Get a look under the covers: Learn tuning best practices for taking advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. This session explains how to migrate from existing data warehouses, create an optimized schema, efficiently load data, use workload management, tune your queries, and use Amazon Redshift's interleaved sorting features.You’ll then hear from a customer who has leveraged Redshift in their industry and how they have adopted many of the best practices. Learn More: https://aws.amazon.com/government-education/
Roko Kruze of vectorized.io describes real-time analytics using Redpanda event streams and ClickHouse data warehouse. 15 December 2021 SF Bay Area ClickHouse Meetup
Streaming Operational Data with MariaDB MaxScaleMariaDB plc
MariaDB experts explain how to stream data using MariaDB MaxScale, a database proxy that can vastly improve your server's transactional data processing without sacrificing scalability, security or speed. In this webinar, learn how to use MaxScale to convert data to JSON documents or AVRO objects, and watch as MariaDB's senior software engineers do a live demo of how to use the Kafka producer.
Watch the webinar here: https://mariadb.com/resources/webinars/streaming-operational-data-mariadb-maxscale
Meta/Facebook's database serving social workloads is running on top of MyRocks (MySQL on RocksDB). This means our performance and reliability depends a lot on RocksDB. Not just MyRocks, but also we have other important systems running on top of RocksDB. We have learned many lessons from operating and debugging RocksDB at scale.
In this session, we will offer an overview of RocksDB, key differences from InnoDB, and share a few interesting lessons learned from production.
This presentation focuses on optimization of queries in MySQL from developer’s perspective. Developers should care about the performance of the application, which includes optimizing SQL queries. It shows the execution plan in MySQL and explain its different formats - tabular, TREE and JSON/visual explain plans. Optimizer features like optimizer hints and histograms as well as newer features like HASH joins, TREE explain plan and EXPLAIN ANALYZE from latest releases are covered. Some real examples of slow queries are included and their optimization explained.
FLiP Into Trino
FLiP into Trino. Flink Pulsar Trino
Pulsar SQL (Trino/Presto)
Remember the days when you could wait until your batch data load was done and then you could run some simple queries or build stale dashboards? Those days are over, today you need instant analytics as the data is streaming in real-time. You need universal analytics where that data is. I will show you how to do this utilizing the latest cloud native open source tools. In this talk we will utilize Trino, Apache Pulsar, Pulsar SQL and Apache Flink to analyze instantly data from IoT, sensors, transportation systems, Logs, REST endpoints, XML, Images, PDFs, Documents, Text, semistructured data, unstructured data, structured data and a hundred data sources you could never dream of streaming before. I will teach how to use Pulsar SQL to run analytics on live data.
Tim Spann
Developer Advocate
StreamNative
David Kjerrumgaard
Developer Advocate
StreamNative
https://www.starburst.io/info/trinosummit/
https://github.com/tspannhw/FLiP-Into-Trino/blob/main/README.md
https://github.com/tspannhw/StreamingAnalyticsUsingFlinkSQL/tree/main/src/main/java
select * from pulsar."public/default"."weather";
Apache Pulsar plus Trio = fast analytics at scale
Deep Dive on ElasticSearch Meetup event on 23rd May '15 at www.meetup.com/abctalks
Agenda:
1) Introduction to NOSQL
2) What is ElasticSearch and why is it required
3) ElasticSearch architecture
4) Installation of ElasticSearch
5) Hands on session on ElasticSearch
Microservices give us many options. We can pick different technologies, mix synchronous and asynchronous integration techniques or embrace different deployment patterns. But they also give us different options in how we think about securing our systems. Done right, and microservices can increase the security of your vital data and processes. Done wrong, and you can increase the surface area of attack. This talk will discuss the importance of defence in depth, discussing the many different ways in which you can secure your fine-grained, distributed architectures.
This is the longer, 90 min version of my Microservices talk, as presented at Velocity 2016 in Santa Clara.
Security is everyone’s job, even if you’re not a specialist. Microservices offer many options for securing your systems. Done right, microservices can increase the security of your vital data and processes. Done wrong, and they can increase the surface area of attack. Sam Newman explores the importance of defense in depth, discussing the many different ways in which you can secure your fine-grained, distributed architectures and outlining a model to show how developers can think about application security and how they can play their part. From there, Sam dives into the specific challenges in microservice architectures and explains how application security principles can be applied to these often much more complex application architectures. You’ll leave with a high-level framework for thinking about application security and tools that help with prevention, detection, response, and recovery, as well as the knowledge of what not to do when breaches happen.
This talk will tell the story of an analytics use case database from a non-OLAP and ACID-compliant RDBMS (MySQL) perspective.
I will cover the basics of the Clickhouse database Sample Clickhouse installation in a lab environment.
We are configuring Clickhouse for essential operations.
We will load the sample data set and monitor it.
We will query and visualize the results.
This talk will also base on how Kubernetes can help Clickhouse implementation via an operator.
Conclusions will include Do's and Don't of this emerging technology. Best practices and some advice around ingesting and analyzing terabytes of data efficiently.
Vitess VReplication: Standing on the Shoulders of a MySQL GiantMatt Lord
Vitess provides a large set of features that allow you to use and manage a scalable set of MySQL database instances across custom partitions or shards of your dataset as if it was a single logical database. One of the key components used within Vitess is called VReplication.
In this talk, we'll cover what VReplication is and how it relates to MySQL replication, including how VReplication leverages the technologies you're already familiar with while expanding on them to add a set of powerful primitives and abstractions that support an ever-growing list of high-level features such as sharding and resharding of tables, materialized views, online DDL, change streams (CDC), and message or job queues.
This talk should leave a MySQL user/operator with a good understanding of what VReplication could do for them and when they may want to use it.
ElasticSearch introduction talk. Overview of the API, functionality, use cases. What can be achieved, how to scale? What is Kibana, how it can benefit your business.
EXPLAIN ANALYZE is a new query profiling tool first released in MySQL 8.0.18. This presentation covers how this new feature works, both on the surface and on the inside, and how you can use it to better understand your queries, to improve them and make them go faster.
This presentation is for everyone who has ever had to understand why a query is executed slower than anticipated, and for everyone who wants to learn more about query plans and query execution in MySQL.
MySQL Administrator
Basic course
- MySQL 개요
- MySQL 설치 / 설정
- MySQL 아키텍처 - MySQL 스토리지 엔진
- MySQL 관리
- MySQL 백업 / 복구
- MySQL 모니터링
Advanced course
- MySQL Optimization
- MariaDB / Percona
- MySQL HA (High Availability)
- MySQL troubleshooting
네오클로바
http://neoclova.co.kr/
Anoop Sam John and Ramkrishna Vasudevan (Intel)
HBase provides an LRU based on heap cache but its size (and so the total data size that can be cached) is limited by Java’s max heap space. This talk highlights our work under HBASE-11425 to allow the HBase read path to work directly from the off-heap area.
Best Practices for Data Warehousing with Amazon Redshift | AWS Public Sector ...Amazon Web Services
Get a look under the covers: Learn tuning best practices for taking advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. This session explains how to migrate from existing data warehouses, create an optimized schema, efficiently load data, use workload management, tune your queries, and use Amazon Redshift's interleaved sorting features.You’ll then hear from a customer who has leveraged Redshift in their industry and how they have adopted many of the best practices. Learn More: https://aws.amazon.com/government-education/
Roko Kruze of vectorized.io describes real-time analytics using Redpanda event streams and ClickHouse data warehouse. 15 December 2021 SF Bay Area ClickHouse Meetup
Streaming Operational Data with MariaDB MaxScaleMariaDB plc
MariaDB experts explain how to stream data using MariaDB MaxScale, a database proxy that can vastly improve your server's transactional data processing without sacrificing scalability, security or speed. In this webinar, learn how to use MaxScale to convert data to JSON documents or AVRO objects, and watch as MariaDB's senior software engineers do a live demo of how to use the Kafka producer.
Watch the webinar here: https://mariadb.com/resources/webinars/streaming-operational-data-mariadb-maxscale
Meta/Facebook's database serving social workloads is running on top of MyRocks (MySQL on RocksDB). This means our performance and reliability depends a lot on RocksDB. Not just MyRocks, but also we have other important systems running on top of RocksDB. We have learned many lessons from operating and debugging RocksDB at scale.
In this session, we will offer an overview of RocksDB, key differences from InnoDB, and share a few interesting lessons learned from production.
This presentation focuses on optimization of queries in MySQL from developer’s perspective. Developers should care about the performance of the application, which includes optimizing SQL queries. It shows the execution plan in MySQL and explain its different formats - tabular, TREE and JSON/visual explain plans. Optimizer features like optimizer hints and histograms as well as newer features like HASH joins, TREE explain plan and EXPLAIN ANALYZE from latest releases are covered. Some real examples of slow queries are included and their optimization explained.
FLiP Into Trino
FLiP into Trino. Flink Pulsar Trino
Pulsar SQL (Trino/Presto)
Remember the days when you could wait until your batch data load was done and then you could run some simple queries or build stale dashboards? Those days are over, today you need instant analytics as the data is streaming in real-time. You need universal analytics where that data is. I will show you how to do this utilizing the latest cloud native open source tools. In this talk we will utilize Trino, Apache Pulsar, Pulsar SQL and Apache Flink to analyze instantly data from IoT, sensors, transportation systems, Logs, REST endpoints, XML, Images, PDFs, Documents, Text, semistructured data, unstructured data, structured data and a hundred data sources you could never dream of streaming before. I will teach how to use Pulsar SQL to run analytics on live data.
Tim Spann
Developer Advocate
StreamNative
David Kjerrumgaard
Developer Advocate
StreamNative
https://www.starburst.io/info/trinosummit/
https://github.com/tspannhw/FLiP-Into-Trino/blob/main/README.md
https://github.com/tspannhw/StreamingAnalyticsUsingFlinkSQL/tree/main/src/main/java
select * from pulsar."public/default"."weather";
Apache Pulsar plus Trio = fast analytics at scale
Deep Dive on ElasticSearch Meetup event on 23rd May '15 at www.meetup.com/abctalks
Agenda:
1) Introduction to NOSQL
2) What is ElasticSearch and why is it required
3) ElasticSearch architecture
4) Installation of ElasticSearch
5) Hands on session on ElasticSearch
Microservices give us many options. We can pick different technologies, mix synchronous and asynchronous integration techniques or embrace different deployment patterns. But they also give us different options in how we think about securing our systems. Done right, and microservices can increase the security of your vital data and processes. Done wrong, and you can increase the surface area of attack. This talk will discuss the importance of defence in depth, discussing the many different ways in which you can secure your fine-grained, distributed architectures.
This is the longer, 90 min version of my Microservices talk, as presented at Velocity 2016 in Santa Clara.
Security is everyone’s job, even if you’re not a specialist. Microservices offer many options for securing your systems. Done right, microservices can increase the security of your vital data and processes. Done wrong, and they can increase the surface area of attack. Sam Newman explores the importance of defense in depth, discussing the many different ways in which you can secure your fine-grained, distributed architectures and outlining a model to show how developers can think about application security and how they can play their part. From there, Sam dives into the specific challenges in microservice architectures and explains how application security principles can be applied to these often much more complex application architectures. You’ll leave with a high-level framework for thinking about application security and tools that help with prevention, detection, response, and recovery, as well as the knowledge of what not to do when breaches happen.
Etsy has been pushing the idea of how we do monitoring.
Beyond just monitoring devices and services, we discuss reducing alert fatigue, the impact of alerts on operations engineers, and how we can learn about monitoring from other industries.
Solomon Hykes from the Docker project explains the principles and operations of the Docker project, and how it deals with extreme levels of scale and openness.
Everyone wants their little application to grow up to be a strong, well-rounded, and useful set of code. We organize, we unit test, we market research, and then we push to production. All is good in the world until now you need two web servers, and multiple back-end servers, and more DB servers than you have fingers. Your code starts to act weird, there are errors in some places but not others. Fires, floods, and locusts all start to appear, and how do you manage it? Let's look at some real-life examples, along with some tools and tips, for managing those fires as your application grows.
Сергей Татаринцев — Тестирование CSS-регрессий с GeminiYandex
Каждый разработчик интерфейсов долгоживущих сервисов сталкивается с регрессиями в вёрстке. Мы научились пользоваться инструментами для unit-тестирования js-кода, но до сих пор плохо понимаем, как тестировать на регрессии вёрстку. И ещё хуже понимаем, как делать это автоматически (continuous integration) и при этом писать небольшие и не очень хрупкие тесты. В этом году мы создали Gemini — инструмент для модульного тестирования вёрстки для нашей библиотеки компонентов. Мы используем его для тестирования внутренней библиотеки компонентов Яндекса, которая лежит в основе большинства наших сервисов (например, Поиска и Картинок). На BEMup я расскажу, как использовать этот инструмент — как разрабатывать тесты и запускать их на локальной машине или в уже существующей экосистеме (Travis CI, Sauce Labs).
Elasticsearch + Cascading for Scalable Log ProcessingCascading
Supreet Oberoi's presentation on "Large scale log processing with Cascading & Elastic Search". Elasticsearch is becoming a popular platform for log analysis with its ELK stack: Elasticsearch for search, Logstash for centralized logging, and Kibana for visualization. Complemented with Cascading, the application development platform for building Data applications on Apache Hadoop, developers can correlate at scale multiple log and data streams to perform rich and complex log processing before making it available to the ELK stack.
App::highlight - a simple grep-like highlighter appAlex Balhatchet
App::highlight is a bit like grep, except that it doesn't filter out lines. In exchange for seeing all the output you get a lot more fun highlighting options to play with, and full Perl regex support of course.
I gave this talk at the London.pm technical meeting in July 2013.
App::highlight is available on Github and CPAN.
'Scalable Logging and Analytics with LogStash'Cloud Elements
Rich Viet, Principal Engineer at Cloud Elements presents 'Scalable Logging and Analytics with LogStash' at All Things API meetup in Denver, CO.
Learn more about scalable logging and analytics using LogStash. This will be an overview of logstash components, including getting started, indexing, storing and getting information from logs.
Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching).
At Etsy we are big fans of graphing and monitoring all the things. We deploy our main site several times a day and our monitoring provides us with the tight feedback loop we need to make this possible. The same goes for changes in the infrastructure which are deployed in a similar fashion of small and frequent changes. For this to work we have build up monitoring that tracks changes and possible problems in every nook and cranny of the Etsy stack, be it a network change, systems or application level performance or how bad the last week of on-call rotation was. The flipside of monitoring all the things however is that we have a myriad of graphs and alerts that can potentially be important and page the on-call engineer at any given time. The risk of running into alert fatigue and a normalization of deviance through not properly scoped checks is rising with this ever increasing size of the monitoring system. This is why we also continuously monitor our monitoring system and ask questions about whether we have all the information at hand when we get paged, if certain alerts actually need to wake someone up or if they are needed at all. I will give a quick overview of how our monitoring stack is built and then give insights into how we gather data about it and use it to make things better. For site operations and more importantly for the human getting paged when something does go wrong.
A presentation about the deployment of an ELK stack at bol.com
At bol.com we use Elasticsearch, Logstash and Kibana in a logsearch system that allows our developers and operations people to easilly access and search thru logevents coming from all layers of its infrastructure.
The presentations explains the initial design and its failures. It continues with explaining the latest design (mid 2014). Its improvements. And finally a set of tips are giving regarding Logstash and Elasticsearch scaling.
These slides were first presented at the Elasticsearch NL meetup on September 22nd 2014 at the Utrecht bol.com HQ.
Organizations continue to adopt Solr because of its ability to scale to meet even the most demanding workflows. Recently, LucidWorks has been leading the effort to identify, measure, and expand the limits of Solr. As part of this effort, we've learned a few things along the way that should prove useful for any organization wanting to scale Solr. Attendees will come away with a better understanding of how sharding and replication impact performance. Also, no benchmark is useful without being repeatable; Tim will also cover how to perform similar tests using the Solr-Scale-Toolkit in Amazon EC2.
(WEB401) Optimizing Your Web Server on AWS | AWS re:Invent 2014Amazon Web Services
Tuning your EC2 web server will help you to improve application server throughput and cost-efficiency as well as reduce request latency. In this session we will walk through tactics to identify bottlenecks using tools such as CloudWatch in order to drive the appropriate allocation of EC2 and EBS resources. In addition, we will also be reviewing some performance optimizations and best practices for popular web servers such as Nginx and Apache in order to take advantage of the latest EC2 capabilities.
KSQL Performance Tuning for Fun and Profit ( Nick Dearden, Confluent) Kafka S...confluent
Ever wondered just how many CPU cores of KSQL Server you need to provision to handle your planned stream processing workload ? Or how many GBits of aggregate network bandwidth, spread across some number of processing threads, you'll need to deal with combined peak throughput of multiple queries ? In this talk we'll first explore the basic drivers of KSQL throughput and hardware requirements, building up to more advanced query plan analysis and capacity-planning techniques, and review some real-world testing results along the way. Finally we will recap how and what to monitor to know you got it right!
Docker Logging and analysing with Elastic StackJakub Hajek
Collecting logs from the entire stateless environment is challenging parts of the application lifecycle. Correlating business logs with operating system metrics to provide insights is a crucial part of the entire organization. What aspects should be considered while you design your logging solutions?
Docker Logging and analysing with Elastic Stack - Jakub Hajek PROIDEA
Collecting logs from the entire stateless environment is challenging parts of the application lifecycle. Correlating business logs with operating system metrics to provide insights is a crucial part of the entire organization. We will see the technical presentation on how to manage a large amount of the data in a typical environment with microservices.
In this presentation from the DDN User Meeting at SC13, Tommy Minyard from the Texas Advanced Computing Center describes TACC's new Corral data storage system.
Watch the video presentation: http://insidehpc.com/2013/11/13/ddn-user-meeting-coming-sc13-nov-18/
AWS re:Invent presentation: Unmeltable Infrastructure at Scale by Loggly SolarWinds Loggly
April 2014 update to this presentation: Loggly removed Storm from its architecture. Details here: https://www.loggly.com/blog/what-we-learned-about-scaling-with-apache-storm/
This is a technical architect's case study of how Loggly has employed the latest social-media-scale technologies as the backbone ingestion processing for our multi-tenant, geo-distributed, and real-time log management system. Given by Jim Nisbet and Philip O'Toole, this presentation describes design details of how we built a second-generation system fully leveraging AWS services including Amazon Route 53 DNS with heartbeat and latency-based routing, multi-region VPCs, Elastic Load Balancing, Amazon Relational Database Service, and a number of pro-active and re-active approaches to scaling computational and indexing capacity.
The talk includes lessons learned in our first generation release, validated by thousands of customers; speed bumps and the mistakes we made along the way; various data models and architectures previously considered; and success at scale: speeds, feeds, and an unmeltable log processing engine.
Logging at OVHcloud :
Logs Data platform est la plateforme de collecte, d'analyse et de gestion centralisée de logs d'OVHcloud. Cette plateforme a pour but de répondre aux challenges que constitue l'indexation de plus de 4000 milliards de logs par une entreprise comme OVHcloud. Cette présentation vous décrira l'architecture générale de Logs Data Platform autour de ses composants centraux Elasticsearch et Graylog et vous décrira les différentes problématiques de scalabilité, disponibilité, performance et d'évolutivité qui sont le quotidien de l'équipe Observability à OVHcloud.
Infrastructure review - Shining a light on the Black BoxMiklos Szel
Scenario: You work as a consultant and a new client has just signed on. Their DBA left suddenly leaving nothing but some outdated documentation in their wiki. After the kick-off meeting you realise that the operations and the development teams know little to none about the databases. They have been encountering intermittent problems with the application’s performance and suspect it’s related to the databases. You are told: "Please fix it ASAP!” So you have your public key installed on their jumphost and they manage to provide you with a 6 character long mysql root password. This is where your journey begins! During this session you will learn some of the best practices around discovering a new environment, finding possible threats and weaknesses and determining what key metrics to focus on for performance and reliability. We will cover architecture, replication, OS and MySQL level configuration, storage engines, failover strategies, backup and restores, monitoring, query tuning and possible ways to save money. The goal at the end of the presentation is to have a prioritized action plan. I will also explain the usage and the output of some tools/wrappers that help during an infrastructure review. Examples include creation of maximum integetr usage reports, table fragmentation and duplicate keys (we will be leveraging multiple Percona Toolkit scripts but also some lesser known tools as well). It is often easy to overlook underlying problems in the infrastructure during day-to-day operations, so this presentation will aim to highlight how to identify and resolve potential bottlenecks with your systems.
Datadog: a Real-Time Metrics Database for One Quadrillion Points/DayC4Media
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.
This talk was presented at Software GR in December 2014.
It covers lessons learned as an engineer on how to successfully be a remote engineer.
Also covered are expectations from teams, management and the business in order to make remote engineering a success.
The Interruptive Nature of Operations (2014, Velocity Barcelona)Avleen Vig
Interruptions are all around us. They impact our work, but they are also the source of our work.
This presentation is a redux of a similar presentation from 2013, with greater details and emphasis on managing interruptions.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
10. 10
Healthy Advice
• Rename your cluster from “elasticsearch” to something else.
When you end up with two Elasticsearch clusters on your network, you’ll
be glad you did.
• Oops, deleted all the indices again!
Set action.destructive_requires_name=true
• Always use SSDs. This is not optional.
• If you’re seeing this talk, you probably need 10G networking too.
• Use curator. We developed our own version before it was available.
11. 11
ACT 1, SC ENE 1
Sizing up your Elasticsearch
Cluster
12. 12
What resources influence cluster make-up?
• CPU
- Cores > clock speed
• Memory
- Number of documents
- Number of shards
• Disk I/O
- SSD sustained write rates
• Network bandwidth
- 10G mandatory on large installations for fast recovery / relocation
13. 13
What resources influence cluster memory?
• Memory
- Segment memory: ~4b RAM per document = ~4Gb per billion log lines
- Field data memory: Approximately same as segment memory
(less for older, less accessed data)
- Filter cache: ~1/4 to 1/2 of segment memory, depending on searches
- All the rest (at least 50% of system memory) for OS file cache
- You can't have enough memory!
14. 14
What resources influence cluster I/O?
• Disk I/O
- SSD sustained write rates
- Calculate shard recovery speed if one node fails:
- Shard size = (Daily storage / number of shards)
- (Shards per node * shard size) / (disk write speed / shards per node)
• Eg: 30Gb shards, 2 shards per node, 250Mbps write speed:
- (2 * 30Gb) / 125Mbps = 8 minutes
• How long are you comfortable losing resilience?
• How many nodes are you comfortable losing?
• Multiple nodes per server increase recovery time
15. 15
What resources influence cluster networking?
• Network bandwidth
- 10G mandatory on large installations for fast recovery / relocation
- 10 minute recovery vs 50+ minute recovery:
• 1G Bottleneck: Network uplink
• 10G Bottleneck: Disk speed
16. 16
ACT 1, SC ENE 2
Sizing up your Logstash
Cluster
21. 21
• Easy to use
• Data saved to ES
• So many metrics!
• No integration
• Costs $$$
• Time to develop
• Integrates with your
systems
• Re-inventing the wheel
• Free (libre, not gratis)
Roll your ownMarvel
22. 22
Monitoring: Elasticsearch
• Metrics are exposed in several places:
- _cat API
Covers most metrics, human readable
- _stats API, _nodes API
Covers everything, JSON, easy to parse
• Send to Graphite
• Create dashboards
23. 23
Monitoring: Systems
• SSD endurance
• Monitor how often Logstash says the pipeline is blocked
If it happens frequently, find out why (mention the possibilities and that
we’ll cover them later)
24. 24
Monitoring: Systems
• Dynamic disk space thresholds
• ((num_servers - failure_capacity) / num_servers) - 15%
- 100 servers
- Allow up to 6 to fail
- Disk space alert threshold = ((100 - 6) / 100) - 15%
Disk space alert threshold = 79%
• Let your configuration management system tune this up and down for
you, as you add and remove nodes from your cluster.
• The additional 15% is to give you some extra time to order or build more
nodes.
26. 26
Scaling Logstash: What impacts performance?
• Line length
• Grok pattern complexity - regex is slow
• Plugins used
• Garbage collection
- Increase heap size
• Hyperthreading
- Measure, then turn it off
27. 27
Scaling Logstash: Measure Twice
• Writing your logs as JSON has little benefit, unless you do away with
grok, kv, etc. Logstash still has to convert the incoming string to a ruby
hash anyway.
29. 29
Scaling Logstash: Garbage Collection
• Defaults are usually OK
• Make sure you’re graphing GC
• Ruby LOVES to generate objects: monitor your GC as you scale
• Write plugins thoughtfully with GC in mind:
- Bad: 1_000_000.times { "This is a string" }
user system total real
time 0.130000 0.000000 0.130000 ( 0.132482)
- Good: foo = 'This is a string'; 1_000_000.times { foo }
user system total real
time 0.060000 0.000000 0.060000 ( 0.055005)
31. 31
Scaling Logstash: Plugin Performance: Baseline
• How to establish a baseline
• Measure again with some filters
• Measure again with more filters
• Establish the costs of each filter
• Community filters are for the general case
- You should write their own for your specific case
- Easy to do
• Run all benchmarks for at least 5 mins, with a large data set
41. 41
Scaling Logstash: Plugin Performance
• kv is slow, we wrote a `splitkv` plugin for query strings, etc:
kvarray = text.split(@field_split).map { |afield|
pairs = afield.split(@value_split)
if pairs[0].nil? || !(pairs[0] =~ /^[0-9]/).nil? || pairs[1].nil? ||
(pairs[0].length < @min_key_length && !@preserve_keys.include?(pairs[0]))
next
end
if !@trimkey.nil?
# 2 if's are faster (0.26s) than gsub (0.33s)
#pairs[0] = pairs[0].slice(1..-1) if pairs[0].start_with?(@trimkey)
#pairs[0].chop! if pairs[0].end_with?(@trimkey)
# BUT! in-place tr is 6% faster than 2 if's (0.52s vs 0.55s)
pairs[0].tr!(@trimkey, '') if pairs[0].start_with?(@trimkey)
end
if !@trimval.nil?
pairs[1].tr!(@trimval, '') if pairs[1].start_with?(@trimval)
end
pairs
}
kvarray.delete_if { |x| x == nil }
return Hash[kvarray]
43. 43
Scaling Logstash: Elasticsearch Output
• Logstash output settings directly impact CPU on Logstash machines
- Increase flush_size from 500 to 5000, or more.
- Increase idle_flush_time from 1s to 5s
- Increase output workers
- Results vary by log lines - test for yourself:
• Make a change, wait 15 minutes, evaluate
• With the default 500 from logstash, we peaked at 50% CPU on the
logstash cluster, and ~40k log lines/sec. Bumping this to 10k, and
increasing the idle_flush_time from 1s to 5s got us over 150k log lines/
sec at 25% CPU.
52. 52
Scaling Logstash: Testing Configuration Changes
describe package('logstash'),
:if => os[:family] == 'redhat' do
it { should be_installed }
end
describe command('chef-client') do
its(:exit_status) { should eq 0 }
end
describe command('logstash -t -f ls.conf.test') do
its(:exit_status) { should eq 0 }
end
describe command('logstash -f ls.conf.test') do
its(:stdout) { should_not match(/parse_fail/) }
end
describe command('restart logstash') do
its(:exit_status) { should eq 0 }
end
describe command('sleep 15') do
its(:exit_status) { should eq 0 }
end
describe service('logstash'),
:if => os[:family] == 'redhat' do
it { should be_enabled }
it { should be_running }
end
describe port(5555) do
it { should be_listening }
end
56. 56
Scaling Logstash: Summary
• Faster CPUs matter
- CPU cores > CPU clock speed
• Increase pipeline size
• Lots of memory
- 18Gb+ to prevent frequent garbage collection
• Scale horizontally
• Add context to your log lines
• Write your own plugins, share with the world
• Benchmark everything
61. 61
Scaling Elasticsearch: What impacts indexing performance?
• Line length and analysis, default mapping
• doc_values - required, not a magic fix:
- Uses more CPU time
- Uses more disk space, disk I/O at indexing
- Helps blowing out memory.
- If you start using too much memory for fielddata, look at the biggest
memory hogs and move them to doc_values
• Available network bandwidth for recovery
65. 65
Scaling Elasticsearch: Where does memory go?
• Example memory distribution with 32Gb heap:
- Field data: 10%
Filter cache: 10%
Index buffer: 500Mb
- Segment cache (~4 bytes per doc):
How many docs can you store per node?
• 32Gb - ( 32G / 10 ) - ( 32G / 10 ) - 500Mb = ~25Gb for segment cache
• 25Gb / 4b = 6.7bn docs across all shards
• 10bn docs / day, 200 shards = 50m docs/shard
1 daily shard per node: 6.7bn / 50m / 1 = 134 days
5 daily shards per node: 6.7bn / 50m / 5 = 26 days
66. 66
Scaling Elasticsearch: Doc Values
• Doc values help reduce memory
• Doc values cost CPU and storage
- Some fields with doc_values:
1.7G Aug 11 18:42 logstash-2015.08.07/7/index/_1i4v_Lucene410_0.dvd
- All fields with doc_values:
106G Aug 13 20:33 logstash-2015.08.12/38/index/_2a9p_Lucene410_0.dvd
• Don't blindly enable Doc Values for every field
- Find your most frequently used fields, and convert them to Doc Values
- curl -s 'http://localhost:9200/_cat/fielddata?v' | less -S
68. 68
Scaling Elasticsearch: Memory
• Run instances with 128Gb or 256Gb RAM
• Configure RAM for optimal hardware configuration
- Haswell/Skylake Xeon CPUs have 4 memory channels
• Multiple instances of Elasticsearch
- Do you name your instances by hostname?
Give each instance it’s own node.name!
70. 70
Scaling Elasticsearch: CPUs
• CPU intensive activities
- Indexing: analysis, merging, compression
- Searching: computations, decompression
• For write-heavy workloads
- Number of CPU cores impacts number of concurrent index operations
- Choose more cores, over higher clock speed
87. 87
Scaling Elasticsearch: Disk I/O
• Uncommon advice
- Good SSDs are important
Cheap SSDs will make you very, very sad
- Don’t use multiple data paths, use RAID 0 instead
Heavy translog writes to one disk will bottleneck
- If you have heavy merging, but CPU and disk I/O to spare:
Extreme case: increase index.merge.scheduler.max_thread_count
(But try not to…)
88. 88
Scaling Elasticsearch: Disk I/O
• Uncommon advice
- Reduced durability
index.translog.durability: async
Translog fsync() every 5s, may be sufficient with replication
- Cluster recovery eats disk I/O
Be prepared to tune it up and down during recovery, eg:
indices.recovery.max_bytes_per_sec: 300mb
cluster.routing.allocation.cluster_concurrent_rebalance: 24
cluster.routing.allocation.node_concurrent_recoveries: 2
- Any amount of consistent I/O wait indicates a suboptimal state
99. 99
Scaling Elasticsearch: Multi-tiered Storage
• Put your most accessed indices across more servers, with more
memory, and faster CPUs.
• Spec out “cold” storage
- SSDs still necessary! Don't even think about spinning platters
- Cram bigger SSDs per server
• Set index.codec: best_compression
• Move indices, re-optimize
• elasticsearch-curator makes this easy
105. 105
Scaling Elasticsearch: Custom Mapping
• A small help.. Unfortunately the server is maxed out now!
Expect this to normally have a bigger impact :-)
107. 107
Scaling Elasticsearch: Indexing Performance
• Increasing bulk thread pool queue can help under bursty indexing
- Be aware of the consequences, you're hiding a performance problem
• Increase index buffer
• Increase refresh time, from 1s to 5s
• Spread indexing requests to multiple hosts
• Increase output workers until you stop seeing improvements
We use num_cpu/2 with transport protocol
• Increase flush_size until you stop seeing improvements
We use 10,000
• Disk I/O performance
108. 108
Scaling Elasticsearch: Indexing Performance
• Indexing protocols
- HTTP
- Node
- Transport
• Transport still slightly more performant, but HTTP has closed the gap.
• Node is generally not worth it. Longer start up, more resources, more
fragile, more work for the cluster.
109. 109
Scaling Elasticsearch: Indexing Performance
• Custom mapping template
- Default template creates an additional not_analyzed .raw field for
every field.
- Every field is analyzed, which eats CPU
- Extra field eats more disk
- Dynamic fields and Hungarian notation
• Use a custom template which has dynamic fields enabled, but has them
not_analyzed
Ditch .raw fields, unless you really need them
• This change dropped Elasticsearch cluster CPU usage from 28% to 15%
110. 110
Scaling Elasticsearch: Indexing Performance
• Message complexity matters.
Adding new lines which are 20k, compared to the average of 1.5k tanked
indexing rate for all log lines:
114. 114
Scaling Elasticsearch: Indices
• Tune shards per index
- num_shards = (num_nodes - failed_node_limit) / (number_of_replicas + 1)
- With 50 nodes, allowing 4 to fail at any time, and 1x replication:
num_shards = (50 - 4) / (1 + 1) = 23
• If your shards are larger than 25Gb, increase shard count accordingly.
• Tune indices.memory.index_buffer_size
- index_buffer_size = num_active_shards * 500Mb
- “Active shards”: any shard updated in the last 5 minutes
115. 115
Scaling Elasticsearch: Indices
• Tune refresh_interval
- Defaults to 1s - way too frequent!
- Increase to 5s
- Tuning higher may cause more disk thrashing
- Goal: Flushing as much as your disk’s buffer than take
• Example: Samsung SM863 SSDs:
- DRAM buffer: 1Gb
- Flush speed: 500Mb/sec
119. 119
5230 segments
29Gb memory
10.5Tb disk space
124 segments
23Gb memory
10.1Tb disk space
OptimizedUnoptimized
Scaling Elasticsearch: Optimize Indices
120. 120
ruby {
code =>
"event['message'] = event['message'].slice!(0,10240)"
}
ruby {
code =>
"if event['message'].length > 10240; then
event['message'] = event['message'].slice!(0,10240)
end"
}
The Thoughtful WayThe Easy Way