Originally delivered as Lightning Talk at Lucene Eurocon 2011 in Barcelona, this quick presentation shows how to use Sematext's SPM service to monitor Solr, OS, JVM, and more.
In the big data world, our data stores communicate over an asynchronous, unreliable network to provide a facade of consistency. However, to really understand the guarantees of these systems, we must understand the realities of networks and test our data stores against them.
Jepsen is a tool which simulates network partitions in data stores and helps us understand the guarantees of our systems and its failure modes. In this talk, I will help you understand why you should care about network partitions and how can we test datastores against partitions using Jepsen. I will explain what Jepsen is and how it works and the kind of tests it lets you create. We will try to understand the subtleties of distributed consensus, the CAP theorem and demonstrate how different data stores such as MongoDB, Cassandra, Elastic and Solr behave under network partitions. Finally, I will describe the results of the tests I wrote using Jepsen for Apache Solr and discuss the kinds of rare failures which were found by this excellent tool.
Cross Datacenter Replication aka CDCR has been a long requested feature in Apache Solr. In this talk, we will discuss CDCR as released in Apache Solr 6.0 and beyond to understand its use-cases, limitations, setup and performance. We will also take a quick look at the future enhancements that can further simplify and scale this feature.
In the big data world, our data stores communicate over an asynchronous, unreliable network to provide a facade of consistency. However, to really understand the guarantees of these systems, we must understand the realities of networks and test our data stores against them.
Jepsen is a tool which simulates network partitions in data stores and helps us understand the guarantees of our systems and its failure modes. In this talk, I will help you understand why you should care about network partitions and how can we test datastores against partitions using Jepsen. I will explain what Jepsen is and how it works and the kind of tests it lets you create. We will try to understand the subtleties of distributed consensus, the CAP theorem and demonstrate how different data stores such as MongoDB, Cassandra, Elastic and Solr behave under network partitions. Finally, I will describe the results of the tests I wrote using Jepsen for Apache Solr and discuss the kinds of rare failures which were found by this excellent tool.
Cross Datacenter Replication aka CDCR has been a long requested feature in Apache Solr. In this talk, we will discuss CDCR as released in Apache Solr 6.0 and beyond to understand its use-cases, limitations, setup and performance. We will also take a quick look at the future enhancements that can further simplify and scale this feature.
Deploying and managing SolrCloud in the cloud using the Solr Scale Toolkitthelabdude
SolrCloud is a set of features in Apache Solr that enable elastic scaling of search indexes using sharding and replication. In this presentation, Tim Potter will demonstrate how to provision, configure, and manage a SolrCloud cluster in Amazon EC2, using a Fabric/boto based solution for automating SolrCloud operations. Attendees will come away with a solid understanding of how to operate a large-scale Solr cluster, as well as tools to help them do it. Tim will also demonstrate these tools live during his presentation. Covered technologies, include: Apache Solr, Apache ZooKeeper, Linux, Python, Fabric, boto, Apache Kafka, Apache JMeter.
This talk was given during Lucene Revolution 2017 and has two goals: first, to discuss the tradeoffs for running Solr on Docker. For example, you get dynamic allocation of operating system caches, but you also get some CPU overhead. We'll keep in mind that Solr nodes tend to be different than your average container: Solr is usually long running, takes quite some RSS and a lot of virtual memory. This will imply, for example, that it makes more sense to use Docker on big physical boxes than on configurable-size VMs (like Amazon EC2).
The second goal is to discuss issues with deploying Solr on Docker and how to work around them. For example, many older (and some of the newer) combinations of Docker, Linux Kernel and JVM have memory leaks. We'll go over Docker operations best practices, such as using container limits to cap memory usage and prevent the host OOM killer from terminating a memory-consuming process - usually a Solr node. Or running Docker in Swarm mode over multiple smaller boxes to limit the spread of a single issue.
Configuring MongoDB HA Replica Set on AWS EC2ShepHertz
It has always been a tedious task to choose the right configuration for MongoDB on AWS EC2
It is always challenging and takes a lots of time to make your system Production Ready.
Here is a quick guide on how to setup MongoDB on AWS EC2.
How to make a simple cheap high availability self-healing solr clusterlucenerevolution
Presented by Stephane Gamard, Chief Technology Officer, Searchbox
In this presentation we aim to show how to make a high availability Solr cloud with 4.1 using only Solr and a few bash scripts. The goal is to present an infrastructure which is self healing using only cheap instances based on ephemeral storage. We will start by providing a comprehensive overview of the relation between collections, Solr cores, shardes, and cluster nodes. We continue by an introduction to Solr 4.x clustering using zookeeper with a particular emphasis on cluster state status/monitoring and solr collection configuration. The core of our presentation will be demonstrated using a live cluster.
We will show how to use cron and bash to monitor the state of the cluster and the state of its nodes. We will then show how we can extend our monitoring to auto generate new nodes, attach them to the cluster, and assign them shardes (selecting between missing shardes or replication for HA). We will show that using a high replication factor it is possible to use ephemeral storage for shards without the risk of data loss, greatly reducing the cost and management of the architecture. Future work discussions, which might be engaged using an open source effort, include monitoring activity of individual nodes as to scale the cluster according to traffic and usage.
Solr cluster with SolrCloud at lucenerevolution (tutorial)searchbox-com
In this presentation we aim to show how to make a high availability Solr cloud with 4.1 using only Solr and a few bash scripts. The goal is to present an infrastructure which is self healing using only cheap instances based on ephemeral storage. We will start by providing a comprehensive overview of the relation between collections, Solr cores, shards and cluster nodes. We continue by an introduction to Solr 4.x clustering using zookeeper with a particular emphasis on cluster state status/monitoring and solr collection configuration. The core of our presentation will be demonstrated using a live cluster. We will show how to use cron and bash to monitor the state of the cluster and the state of its nodes. We will then show how we can extend our monitoring to auto generate new nodes, attach them to the cluster, and assign them shardes (selecting between missing shardes or replication for HA). We will show that using a high replication factor it is possible to use ephemeral storage for shards without the risk of data loss, greatly reducing the cost and management of the architecture. Future work discussions, which might be engaged using an open source effort, include monitoring activity of individual nodes as to scale the cluster according to traffic and usage.
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.
So we're running Apache ZooKeeper. Now What? By Camille Fournier Hakka Labs
The ZooKeeper framework was originally built at Yahoo! to make it easy for the company’s applications to access configuration information in a robust and easy-to-understand way, but it has since grown to offer a lot of features that help coordinate work across distributed clusters. Apache Zookeeper became a de-facto standard for coordination service and used by Storm, Hadoop, HBase, ElasticSearch and other distributed computing frameworks.
Scaling Through Partitioning and Shard Splitting in Solr 4thelabdude
Over the past several months, Solr has reached a critical milestone of being able to elastically scale-out to handle indexes reaching into the hundreds of millions of documents. At Dachis Group, we've scaled our largest Solr 4 index to nearly 900M documents and growing. As our index grows, so does our need to manage this growth.
In practice, it's common for indexes to continue to grow as organizations acquire new data. Over time, even the best designed Solr cluster will reach a point where individual shards are too large to maintain query performance. In this Webinar, you'll learn about new features in Solr to help manage large-scale clusters. Specifically, we'll cover data partitioning and shard splitting.
Partitioning helps you organize subsets of data based on data contained in your documents, such as a date or customer ID. We'll see how to use custom hashing to route documents to specific shards during indexing. Shard splitting allows you to split a large shard into 2 smaller shards to increase parallelism during query execution.
Attendees will come away from this presentation with a real-world use case that proves Solr 4 is elastically scalable, stable, and is production ready.
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.
This talk was given during DockerCon EU 2018.
It ain't just a whim - to be able to continue innovating, we’ve moved our good old static production to containers. We needed to be elastic, fast, reliable and production ready at any time - that's why we chose Docker. But like in most enterprises, lots of our apps run on the JVM and most JVMs’ ergonomics assume they “own” the server they are running on. So how do you containerize JVM apps? Should you really increase JVM heap if you have spare memory? What about OS caches? What are the differences between JDK 8, 9 and 10 when it comes to container awareness? Outages because of out of memory errors? Slowness because of long garbage collection and poor environment visibility? Long story short, in this session, we’ll look at the gotchas of running JVM apps in containers and teach you how to avoid costly mistakes.
Top 3 things attendees will learn:
1. Key differences between various JVM versions relevant for containerized Java apps.
2. Best practices for running JVM in containers.
3. Avoiding common pitfalls when running containerized JVM applications.
Building a near real time search engine & analytics for logs using solrlucenerevolution
Presented by Rahul Jain, System Analyst (Software Engineer), IVY Comptech Pvt Ltd
Consolidation and Indexing of logs to search them in real time poses an array of challenges when you have hundreds of servers producing terabytes of logs every day. Since the log events mostly have a small size of around 200 bytes to few KBs, makes it more difficult to handle because lesser the size of a log event, more the number of documents to index. In this session, we will discuss the challenges faced by us and solutions developed to overcome them. The list of items that will be covered in the talk are as follows.
Methods to collect logs in real time.
How Lucene was tuned to achieve an indexing rate of 1 GB in 46 seconds
Tips and techniques incorporated/used to manage distributed index generation and search on multiple shards
How choosing a layer based partition strategy helped us to bring down the search response times.
Log analysis and generation of analytics using Solr.
Design and architecture used to build the search platform.
National Strategic Alliance on Sustainable Agriculture Principles GlobalHunt Foundation
GlobalHunt Foundation initiated the National Strategic Alliance on Sustainable Agricultural Principles (SAP). This outcome report showcases the proceedings of the multi stakeholder meeting to discuss and strategize on key issues on pertaining to the alliance. AsaCSRandSustainabilityResearch,ConsultingandAdvisoryorganizationGlobalHuntFoundationthroughtheformulationofStrategicAllianceforSustainableAgricultureendeavorsobringsustainabilityintherealmofagriculture.Thepurposebehindtheallianceistoformulatesustainableprinciplesofagriculturethathelpsindesigningprogrammesacrosspan-Indiatowardstherevivingandsecuringofsustainableindigenousfarmingpractices,launching programmes that will enable upscaling of small and marginal farmers,preservation of indigenous knowledge,skills and enhancing ethical value chain.
Deploying and managing SolrCloud in the cloud using the Solr Scale Toolkitthelabdude
SolrCloud is a set of features in Apache Solr that enable elastic scaling of search indexes using sharding and replication. In this presentation, Tim Potter will demonstrate how to provision, configure, and manage a SolrCloud cluster in Amazon EC2, using a Fabric/boto based solution for automating SolrCloud operations. Attendees will come away with a solid understanding of how to operate a large-scale Solr cluster, as well as tools to help them do it. Tim will also demonstrate these tools live during his presentation. Covered technologies, include: Apache Solr, Apache ZooKeeper, Linux, Python, Fabric, boto, Apache Kafka, Apache JMeter.
This talk was given during Lucene Revolution 2017 and has two goals: first, to discuss the tradeoffs for running Solr on Docker. For example, you get dynamic allocation of operating system caches, but you also get some CPU overhead. We'll keep in mind that Solr nodes tend to be different than your average container: Solr is usually long running, takes quite some RSS and a lot of virtual memory. This will imply, for example, that it makes more sense to use Docker on big physical boxes than on configurable-size VMs (like Amazon EC2).
The second goal is to discuss issues with deploying Solr on Docker and how to work around them. For example, many older (and some of the newer) combinations of Docker, Linux Kernel and JVM have memory leaks. We'll go over Docker operations best practices, such as using container limits to cap memory usage and prevent the host OOM killer from terminating a memory-consuming process - usually a Solr node. Or running Docker in Swarm mode over multiple smaller boxes to limit the spread of a single issue.
Configuring MongoDB HA Replica Set on AWS EC2ShepHertz
It has always been a tedious task to choose the right configuration for MongoDB on AWS EC2
It is always challenging and takes a lots of time to make your system Production Ready.
Here is a quick guide on how to setup MongoDB on AWS EC2.
How to make a simple cheap high availability self-healing solr clusterlucenerevolution
Presented by Stephane Gamard, Chief Technology Officer, Searchbox
In this presentation we aim to show how to make a high availability Solr cloud with 4.1 using only Solr and a few bash scripts. The goal is to present an infrastructure which is self healing using only cheap instances based on ephemeral storage. We will start by providing a comprehensive overview of the relation between collections, Solr cores, shardes, and cluster nodes. We continue by an introduction to Solr 4.x clustering using zookeeper with a particular emphasis on cluster state status/monitoring and solr collection configuration. The core of our presentation will be demonstrated using a live cluster.
We will show how to use cron and bash to monitor the state of the cluster and the state of its nodes. We will then show how we can extend our monitoring to auto generate new nodes, attach them to the cluster, and assign them shardes (selecting between missing shardes or replication for HA). We will show that using a high replication factor it is possible to use ephemeral storage for shards without the risk of data loss, greatly reducing the cost and management of the architecture. Future work discussions, which might be engaged using an open source effort, include monitoring activity of individual nodes as to scale the cluster according to traffic and usage.
Solr cluster with SolrCloud at lucenerevolution (tutorial)searchbox-com
In this presentation we aim to show how to make a high availability Solr cloud with 4.1 using only Solr and a few bash scripts. The goal is to present an infrastructure which is self healing using only cheap instances based on ephemeral storage. We will start by providing a comprehensive overview of the relation between collections, Solr cores, shards and cluster nodes. We continue by an introduction to Solr 4.x clustering using zookeeper with a particular emphasis on cluster state status/monitoring and solr collection configuration. The core of our presentation will be demonstrated using a live cluster. We will show how to use cron and bash to monitor the state of the cluster and the state of its nodes. We will then show how we can extend our monitoring to auto generate new nodes, attach them to the cluster, and assign them shardes (selecting between missing shardes or replication for HA). We will show that using a high replication factor it is possible to use ephemeral storage for shards without the risk of data loss, greatly reducing the cost and management of the architecture. Future work discussions, which might be engaged using an open source effort, include monitoring activity of individual nodes as to scale the cluster according to traffic and usage.
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.
So we're running Apache ZooKeeper. Now What? By Camille Fournier Hakka Labs
The ZooKeeper framework was originally built at Yahoo! to make it easy for the company’s applications to access configuration information in a robust and easy-to-understand way, but it has since grown to offer a lot of features that help coordinate work across distributed clusters. Apache Zookeeper became a de-facto standard for coordination service and used by Storm, Hadoop, HBase, ElasticSearch and other distributed computing frameworks.
Scaling Through Partitioning and Shard Splitting in Solr 4thelabdude
Over the past several months, Solr has reached a critical milestone of being able to elastically scale-out to handle indexes reaching into the hundreds of millions of documents. At Dachis Group, we've scaled our largest Solr 4 index to nearly 900M documents and growing. As our index grows, so does our need to manage this growth.
In practice, it's common for indexes to continue to grow as organizations acquire new data. Over time, even the best designed Solr cluster will reach a point where individual shards are too large to maintain query performance. In this Webinar, you'll learn about new features in Solr to help manage large-scale clusters. Specifically, we'll cover data partitioning and shard splitting.
Partitioning helps you organize subsets of data based on data contained in your documents, such as a date or customer ID. We'll see how to use custom hashing to route documents to specific shards during indexing. Shard splitting allows you to split a large shard into 2 smaller shards to increase parallelism during query execution.
Attendees will come away from this presentation with a real-world use case that proves Solr 4 is elastically scalable, stable, and is production ready.
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.
This talk was given during DockerCon EU 2018.
It ain't just a whim - to be able to continue innovating, we’ve moved our good old static production to containers. We needed to be elastic, fast, reliable and production ready at any time - that's why we chose Docker. But like in most enterprises, lots of our apps run on the JVM and most JVMs’ ergonomics assume they “own” the server they are running on. So how do you containerize JVM apps? Should you really increase JVM heap if you have spare memory? What about OS caches? What are the differences between JDK 8, 9 and 10 when it comes to container awareness? Outages because of out of memory errors? Slowness because of long garbage collection and poor environment visibility? Long story short, in this session, we’ll look at the gotchas of running JVM apps in containers and teach you how to avoid costly mistakes.
Top 3 things attendees will learn:
1. Key differences between various JVM versions relevant for containerized Java apps.
2. Best practices for running JVM in containers.
3. Avoiding common pitfalls when running containerized JVM applications.
Building a near real time search engine & analytics for logs using solrlucenerevolution
Presented by Rahul Jain, System Analyst (Software Engineer), IVY Comptech Pvt Ltd
Consolidation and Indexing of logs to search them in real time poses an array of challenges when you have hundreds of servers producing terabytes of logs every day. Since the log events mostly have a small size of around 200 bytes to few KBs, makes it more difficult to handle because lesser the size of a log event, more the number of documents to index. In this session, we will discuss the challenges faced by us and solutions developed to overcome them. The list of items that will be covered in the talk are as follows.
Methods to collect logs in real time.
How Lucene was tuned to achieve an indexing rate of 1 GB in 46 seconds
Tips and techniques incorporated/used to manage distributed index generation and search on multiple shards
How choosing a layer based partition strategy helped us to bring down the search response times.
Log analysis and generation of analytics using Solr.
Design and architecture used to build the search platform.
National Strategic Alliance on Sustainable Agriculture Principles GlobalHunt Foundation
GlobalHunt Foundation initiated the National Strategic Alliance on Sustainable Agricultural Principles (SAP). This outcome report showcases the proceedings of the multi stakeholder meeting to discuss and strategize on key issues on pertaining to the alliance. AsaCSRandSustainabilityResearch,ConsultingandAdvisoryorganizationGlobalHuntFoundationthroughtheformulationofStrategicAllianceforSustainableAgricultureendeavorsobringsustainabilityintherealmofagriculture.Thepurposebehindtheallianceistoformulatesustainableprinciplesofagriculturethathelpsindesigningprogrammesacrosspan-Indiatowardstherevivingandsecuringofsustainableindigenousfarmingpractices,launching programmes that will enable upscaling of small and marginal farmers,preservation of indigenous knowledge,skills and enhancing ethical value chain.
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.
Sulfur oxides are produced from the burning of fossil fuels, mainly coal and oil, and the smelting of metal ores that contain sulfur.
Emissions of sulfur oxides cause serious impacts on human health and the environment, both directly and as a result of the way they react with other substances in the air.
Sulfur oxides are main precursors of atmospheric acidification, aerosol generation, and acidic dry and wet deposition.
There are many methods available for controlling the emission of SO2. Such as:
extraction of sulfur from fuel oils.
Sulfur reduction within combustion chamber.
Treating of flue gases.
DRY METHODS:
Mainly in industries dry, elevated temperature removal processes are used as cold plume is not formed and problem of handling large amount of slurry in flue gases is avoided.
But there are technical issues resulting in such method making wet method more applicable in industries.
Adsorption of SO2 by metal oxides to from stable sulphites or sulphates with subsequent regeneration.
-Alkalized Alumina Process
-Manganese Oxide Process
Adsorption on activated carbon followed by regeneration and conversion of concentrated SO2 to sulphuric acid or elemental sulphur.
-The Reinluft Process
ALKALIZED ALUMINA PROCESS:
Also called as Cyclic Adsorption Process.
It was developed by U.S Bureau of Mines.
Adsorbent used : Sodium Aluminate (Na2O.Al2O3)-it is porous form.
This process uses Sodium Aluminate (Na2O.Al2O3) to remove SO2 in fluidized bed at 315°C.
Na2O.Al2O3 + SO2 + ½ O2 → Na2SO4 + Al2SO3
The product of above reaction is then contacted with a reducing gas such as H2 in a regenerator at 680°C to produce H2S.
Na2SO4 + Al2O3 + 4H2 → Na2O.Al2O3 + H2S + 3H2O
Sodium Aluminate is recycled back and H2S is sent to Claus Process for producing Sulphur.
Working with deeply nested documents in Apache SolrAnshum Gupta
From my joint talk with Alisa Zhila at Lucene/Solr Revolution 2016 in Boston. The talk covers the following:
- Hierarchical Data/Nested Documents
- Indexing Nested Documents
- Querying Nested Documents
- Faceting on Nested Documents
Air Quality Sampling and Monitoring: Stack sampling, instrumentation and methods of analysis of SO2, CO etc, legislation for control of air pollution and automobile
pollution
1 1/2 years ago we have rolled out a new integrated full-text search engine for our Intranet based on Apache Solr. The search engine integrates various data sources such as file systems, wikis, internal websites and web applications, shared calendars, our corporate database, CRM system, email archive, task management and defect tracking etc. This talk is an experience report about some of the good things, the bad things and the surprising things we have encountered over two years of developing with, operating and using a Intranet search engine based on Apache Solr.
After setting the scene, we will discuss some interesting requirements that we have for our search engine and how we solved them with Apache Solr (or at least tried to solve). Using these concrete examples, we will discuss some interesting features and limitations of Apache Solr.
In the second part of the talk, we will tell a couple of "war stories" and walk through some interesting, annoying and surprising problems that we faced, how we analyzed the issues, identified the cause of the problems and eventually solved them.
The talk is aimed at software developers and architects with some basic knowledge about Apache Solr, the Apache Lucene project familiy or similar full-text search engines. It is not an introduction into Apache Solr and we will dive right into the interesting and juicy bits.
UnConference for Georgia Southern Computer Science March 31, 2015Christopher Curtin
I presented to the Georgia Southern Computer Science ACM group. Rather than one topic for 90 minutes, I decided to do an UnConference. I presented them a list of 8-9 topics, let them vote on what to talk about, then repeated.
Each presentation was ~8 minutes, (Except Career) and was by no means an attempt to explain the full concept or technology. Only to wake up their interest.
Planning For High Performance Web ApplicationYue Tian
This slide is prepared for Beijing Open Party (a monthly unconference in Beijing China). And it's covered some important points when you are building a scalable web sites. And few page of this slide is in Chinese.
This session is for you if you want to learn tips and techniques that are used to optimize database development with special emphasis on SQL Server 2005. If you write lot of stored procedures and want to learn the tools of a DBA, this is the session for you. If you are new to SQL Server development environment, you will learn how the various constructs compare to each other and better performance can be produced every time with a brief introduction to understanding Execution Plans.
Ingesting Over Four Million Rows Per Second With QuestDB Timeseries Database ...javier ramirez
How would you build a database to support sustained ingestion of several hundreds of thousands rows per second while running near real-time queries on top?
In this session I will go over some of the technical decisions and trade-offs we applied when building QuestDB, an open source time-series database developed mainly in JAVA, and how we can achieve over four million row writes per second on a single instance without blocking or slowing down the reads. There will be code and demos, of course.
We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
The rise of cloud and containers has led to systems that are much more distributed and dynamic in nature. Highly elastic microservice and serverless architectures mean containers spin up on demand and scale to zero when that demand goes away. This generates a continous stream of infrastructure data.
On the business side, we have started storing lot of data and this data contains enormours information, specially when married with infrastructure data this gives holistic health information of the entire platform. We will talk about how to achieve this kind of fine-grained observability at scale in real-time.
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...Amazon Web Services
Learn how to deploy a managed Presto environment to interactively query log data on AWS
Organizations often need to quickly analyze large amounts of data, such as logs, generated from a wide variety of sources and formats. However, traditional approaches require a lot of time and effort designing complex data transformation and loading processes; and configuring data warehouses. Using AWS, you can start querying your datasets within minutes
In this webinar you will learn how you can deploy a managed Presto environment in minutes to interactively query log data using plain ANSI SQL. Presto is a popular open source SQL engine for running interactive analytic queries against data sources of all sizes. We will talk about common use cases and best practices for running Presto on Amazon EMR.
Learning Objectives:
• Learn how to deploy a managed Presto environment running on Amazon EMR
• Understand best practices for running Presto on Amazon EMR, including use of Amazon EC2 Spot instances
• Learn how other customers are using Presto to analyze large data sets
Similar to Solr Performance Monitoring with SPM (20)
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
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.
This talk was given during DevOps Con 2017.
Have you ever spent time digging through various terminals, greping, lessing, awking and trying to find that few log lines that may be important? Have you every done that under time pressure, because mission critical services were not working? Have you every heard from your developers that they can’t tell you anything, because they don’t have access to application logs? Have you ever considered a centralized storage for logs, but time and resources are not on your side?
If you said yes, to any of the above questions, than this talk is for you. During the talk we’ll introduce you to the world of log centralization and analysis, both when it comes to open source, but also commercial tools. We will go from top to bottom and learn how to setup log centralization and analysis for servers, virtualized environments and containers. We will get from log shipping, through centralized buffering to storage and analysis to show you, that having a centralized log analysis tool is not a rocket science.
Finally, you will see how useful is to combine the logs from all your servers in a single place for blazingly fast correlation.
An updated talk about how to use Solr for logs and other time-series data, like metrics and social media. In 2016, Solr, its ecosystem, and the operating systems it runs on have evolved quite a lot, so we can now show new techniques to scale and new knobs to tune.
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.
Running High Performance and Fault Tolerant Elasticsearch Clusters on DockerSematext Group, Inc.
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)Sematext Group, Inc.
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 ...Sematext Group, Inc.
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 overhead
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.
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.
From Zero to Hero - Centralized Logging with Logstash & ElasticsearchSematext Group, Inc.
Originally presented at DevOpsDays Warsaw 2014. How to set up centralized logging either using ELK stack - Logstash, Elasticsearch, and Kibana or using Logsene.
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/
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.
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
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.
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
8. Solr Caches see the increase! did you know you had these dips?
9. Search Rate & Latency Select 1 or more Solr instances Select 1 or more Request Handlers Select any time periods Compare 2 time periods Select data granularity
10. Warmup Warmup time broken by warmup type – caches and searcher Select 1 or more Solr Cores