Understanding what is a region for HBase, why those transitions, how to troubleshoot and fix potential problems that may arise from this important HBase internal operation.
This document provides an overview of bag-of-words models for image classification. It discusses how bag-of-words models originated from texture recognition and document classification. Images are represented as histograms of visual word frequencies. A visual vocabulary is learned by clustering local image features, and each cluster center becomes a visual word. Both discriminative methods like support vector machines and generative methods like Naive Bayes are used to classify images based on their bag-of-words representations.
This document summarizes the key concepts and components of Gremlin's graph traversal machinery:
- Gremlin uses a traversal language to express graph queries via step composition, with steps mapping traversers between domains.
- Traversals are compiled to bytecode and optimized by traversal strategies before being executed by the Gremlin machine.
- The Gremlin machine consists of steps implementing functions that process traverser streams. Their composition forms the traversal.
- Gremlin is language-agnostic, with language variants translating to a shared bytecode that interacts with the Java-based implementation.
Guava's Event Bus provides a publish-subscribe style communication system between components within a Java application. It allows loose coupling by avoiding tight dependencies between publisher and subscriber components. The Event Bus implementation handles event dispatching and routing events to subscribed listeners based on the event type. Components publish events by posting them to the Event Bus and subscribe to receive events by registering listener methods annotated with @Subscribe and the event parameter type.
Real Time Streaming Data with Kafka and TensorFlow (Yong Tang, MobileIron) Ka...confluent
This document discusses using Kafka and TensorFlow for real-time streaming machine learning. It introduces TensorFlow 2.0 capabilities for data processing and machine learning. It then discusses challenges in integrating streaming data with machine learning frameworks and formats. It proposes using KafkaDataset, a TensorFlow dataset for reading and writing from Kafka. KafkaDataset allows streaming data in and out of TensorFlow models for real-time inference and prediction.
Dokumen tersebut membahas tentang pengumpulan data yang terdiri dari beberapa item yang dicentang dan beberapa item yang tidak dicentang serta mengandung beberapa subitem.
The document presents a new classification algorithm called Tree Bagging and Weighted Clustering (TBWC) that combines decision tree learning and clustering. TBWC selects important attributes using decision tree bagging, then weights the attributes and uses them to generate clusters for classifying new data. Experimental results show TBWC achieves higher accuracy than decision trees or clustering alone on various datasets, especially those with multiple classes, while also reducing the number of attributes used.
1. The context diagram shows the key external entities that interact with the order system - customers, warehouse, and accounting. It displays the major information flows between these entities and the system.
2. The level-0 DFD of the order system contains 5 main processes: 1) check status, 2) issue status messages, 3) generate shipping order, 4) manage accounts receivable, and 5) produce reports. It also includes 2 data stores - pending orders and accounts receivable - and displays the high-level data flows between the entities, processes, and data stores.
This document provides an overview of bag-of-words models for image classification. It discusses how bag-of-words models originated from texture recognition and document classification. Images are represented as histograms of visual word frequencies. A visual vocabulary is learned by clustering local image features, and each cluster center becomes a visual word. Both discriminative methods like support vector machines and generative methods like Naive Bayes are used to classify images based on their bag-of-words representations.
This document summarizes the key concepts and components of Gremlin's graph traversal machinery:
- Gremlin uses a traversal language to express graph queries via step composition, with steps mapping traversers between domains.
- Traversals are compiled to bytecode and optimized by traversal strategies before being executed by the Gremlin machine.
- The Gremlin machine consists of steps implementing functions that process traverser streams. Their composition forms the traversal.
- Gremlin is language-agnostic, with language variants translating to a shared bytecode that interacts with the Java-based implementation.
Guava's Event Bus provides a publish-subscribe style communication system between components within a Java application. It allows loose coupling by avoiding tight dependencies between publisher and subscriber components. The Event Bus implementation handles event dispatching and routing events to subscribed listeners based on the event type. Components publish events by posting them to the Event Bus and subscribe to receive events by registering listener methods annotated with @Subscribe and the event parameter type.
Real Time Streaming Data with Kafka and TensorFlow (Yong Tang, MobileIron) Ka...confluent
This document discusses using Kafka and TensorFlow for real-time streaming machine learning. It introduces TensorFlow 2.0 capabilities for data processing and machine learning. It then discusses challenges in integrating streaming data with machine learning frameworks and formats. It proposes using KafkaDataset, a TensorFlow dataset for reading and writing from Kafka. KafkaDataset allows streaming data in and out of TensorFlow models for real-time inference and prediction.
Dokumen tersebut membahas tentang pengumpulan data yang terdiri dari beberapa item yang dicentang dan beberapa item yang tidak dicentang serta mengandung beberapa subitem.
The document presents a new classification algorithm called Tree Bagging and Weighted Clustering (TBWC) that combines decision tree learning and clustering. TBWC selects important attributes using decision tree bagging, then weights the attributes and uses them to generate clusters for classifying new data. Experimental results show TBWC achieves higher accuracy than decision trees or clustering alone on various datasets, especially those with multiple classes, while also reducing the number of attributes used.
1. The context diagram shows the key external entities that interact with the order system - customers, warehouse, and accounting. It displays the major information flows between these entities and the system.
2. The level-0 DFD of the order system contains 5 main processes: 1) check status, 2) issue status messages, 3) generate shipping order, 4) manage accounts receivable, and 5) produce reports. It also includes 2 data stores - pending orders and accounts receivable - and displays the high-level data flows between the entities, processes, and data stores.
Machine Learning on Your Hand - Introduction to Tensorflow Lite PreviewModulabs
TF Dev Summit × Modulabs : Learn by Run !
Machine Learning on Your Hand - Introduction to Tensorflow Lite Preview (발표자 : 강재욱)
※ 모두의연구소 페이지 : https://www.facebook.com/lab4all/
※ 모두의연구소 커뮤니티 그룹 : https://www.facebook.com/groups/modulabs
As a young aspiring scientist, social media is one of the outlets to disseminate your work and connect to the community. This talk gives hints on the benefits and risks of science on social media. Talk at the ICSE 2022 New Faculty Symposium.
This document provides an overview of Latent Dirichlet Allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. It defines key terminology for LDA including documents, words, topics, and distributions. The document then explains LDA's graphical model and generative process, which represents documents as mixtures over latent topics and generates words probabilistically from topics. Variational inference is introduced as an approach for approximating the intractable posterior distribution over topics and learning model parameters.
QCONSF - ACID Is So Yesterday: Maintaining Data Consistency with SagasChris Richardson
This is a presentation I gave at QCONSF 2017
The services in a microservice architecture must be loosely coupled and so cannot share database tables. What’s more, two phase commit (a.k.a. a distributed transaction) is not a viable option for modern applications. Consequently, a microservices application must use the Saga pattern, which maintains data consistency using a series of local transactions.
In this presentation, you will learn how sagas work and how they differ from traditional transactions. We describe how to use sagas to develop business logic in a microservices application. You will learn effective techniques for orchestrating sagas and how to use messaging for reliability. We will describe the design of a saga framework for Java and show a sample application.
This is the slide of a session at the biggest tech conference in south Taiwan.
The session would introduce how to improve efficiency on the web application & database side.
This slide use Mandarin
The document discusses using a vector database to enable question answering with custom data. Key points:
- Data is converted to vector embeddings and stored in a vector database like Pinecone to allow for similarity searches.
- When a user asks a question, it is converted to a vector and queried against the database to retrieve similar content to provide as input to a language model for generating an answer.
- The OpenAI API can also be used to build an assistant using a language model, where custom data is loaded to enable answering questions about that data as a "support manager."
Introduction of basic building blocks in regular expressions example repetition token, anchor token, character token etc. Includes some challenges. (solutions included as well)
Announcing Amazon Athena - Instantly Analyze Your Data in S3 Using SQLAmazon Web Services
This document provides an overview and introduction to Amazon Athena, including:
- Athena is an interactive query service that allows users to analyze data directly from Amazon S3 using standard SQL.
- It is serverless, requiring no infrastructure management and with zero spin up time.
- Athena supports a variety of data formats and allows querying data directly from S3 without needing to load it elsewhere.
- Customers can use Athena to analyze large amounts of data in S3 in a cost effective and easy to use manner.
The document provides advice and resources for getting started with programming. It recommends finding a mentor and learning with friends to avoid discouragement. Several online resources are listed for learning programming fundamentals and competitive coding. The key is to try many things before giving up and keep learning new technologies to discover your interests. Starting with what interests you initially and keeping trying without giving up are advised.
The document describes PowerLyra, a system for differentiated graph computation and partitioning on skewed graphs. PowerLyra uses hybrid partitioning and computation strategies to balance locality and parallelism. The hybrid partitioning strategy, called Hybrid-cut, partitions low-degree vertices based on edges (like edge-cut) for locality, and partitions high-degree vertices based on vertices (like vertex-cut) for parallelism. The hybrid computation model processes high-degree vertices in a distributed manner for parallelism, and processes low-degree vertices locally for locality. PowerLyra also includes an optimization called zoning that groups vertices by type to improve data locality during communication.
Packaged enterprise apps are different from custom apps, and that means testing has to be different too. During this webinar we discussed these differences and provided strategies for supporting every aspect of implementing, testing, and running large enterprise applications.
Continuous representations of words and documents, which is recently referred to as Word Embeddings, have recently demonstrated large advancements in many of the Natural language processing tasks.
In this presentation we will provide an introduction to the most common methods of learning these representations. As well as previous methods in building these representations before the recent advances in deep learning, such as dimensionality reduction on the word co-occurrence matrix.
Moreover, we will present the continuous bag of word model (CBOW), one of the most successful models for word embeddings and one of the core models in word2vec, and in brief a glance of many other models of building representations for other tasks such as knowledge base embeddings.
Finally, we will motivate the potential of using such embeddings for many tasks that could be of importance for the group, such as semantic similarity, document clustering and retrieval.
This document discusses building a social network from scratch using microservices and Kubernetes. It outlines the technologies used in the DOF HUNT stack including Golang and Python for backends, Flutter for mobile, MySQL and MongoDB for databases, Elasticsearch for search, Redis for caching, NATS for messaging, and Linkerd for service mesh. It also covers monitoring and tracing with OpenCensus and Jaeger, and logging with Fluentd and Elasticsearch. Clean architecture is employed with modules separated into transport, handler, repository, and storage layers to allow easy transition between monolith and microservices.
Representation Learning of Vectors of Words and PhrasesFelipe Moraes
Talk about representation learning using word vectors such as Word2Vec, Paragraph Vector. Also introduced to neural network language models. Expose some applications using NNLM such as sentiment analysis and information retrieval.
Kotlin and Domain-Driven Design: A perfect match - Kotlin Meetup MunichFlorian Benz
Kotlin helps when pushing for Domain-Driven Design. A common complaint from Java developers is that wrapping and especially mapping values adds too much boilerplate. I rarely hear this from Kotlin developers. This talk presents some of our experience with Kotlin and Domain-Driven Design.
The following article was the basis for this talk:
https://dev.to/flbenz/kotlin-and-domain-driven-design-value-objects-4m32
Research on character level language modelling using LSTM for semi-supervised learning. The objective is learning inner layer representations of the language model for transfer learning into a classification one.
Generalizing NLP processes using Bi-directional LSTMs to learn character(byte) level embeddings of financial news headlines up too 8 bits ( 2**8 -1) in order to study the relationship between character vectors in financial news headlines in order to transfer learning in to classification models using UTF-8 encoding. Many traditional NLP steps (lemmatize, POS, NER, stemming...) are skipped when diving to byte level making the process more universal in terms of scope then task specific.
Configurações distribuídas com Spring Cloud ConfigEmmanuel Neri
Com as arquiteturas voltadas à microserviços um possível problema que possa vir a ocorrer durante o mantimento das aplicações é o gerenciamento de configurações, principalmente falando de aplicações Spring Boot onde temos grande parte das configurações baseados em arquivos, pode-se gerar um cenário repetitivo e desorganizado quando aplicado em várias aplicações. Com isso, essa palestra tem como objetivo apresentar uma solução para centralização dos arquivos de configuração em um serviço externo, possibilitando versionamento, reaproveitamento entre as aplicações, atualizações em runtime e criptografia nas propriedades de configurações utilizando o Spring Cloud Config.
This document provides an overview and instructions for installing and configuring ProxySQL. It discusses:
1. What ProxySQL is and its functions like load balancing and query caching
2. How to install ProxySQL on CentOS and configure the /etc/proxysql.cnf file
3. How to set up the ProxySQL schema to define servers, users, variables and other settings needed for operation
4. How to test ProxySQL functions like server status changes and benchmark performance
Machine Learning on Your Hand - Introduction to Tensorflow Lite PreviewModulabs
TF Dev Summit × Modulabs : Learn by Run !
Machine Learning on Your Hand - Introduction to Tensorflow Lite Preview (발표자 : 강재욱)
※ 모두의연구소 페이지 : https://www.facebook.com/lab4all/
※ 모두의연구소 커뮤니티 그룹 : https://www.facebook.com/groups/modulabs
As a young aspiring scientist, social media is one of the outlets to disseminate your work and connect to the community. This talk gives hints on the benefits and risks of science on social media. Talk at the ICSE 2022 New Faculty Symposium.
This document provides an overview of Latent Dirichlet Allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. It defines key terminology for LDA including documents, words, topics, and distributions. The document then explains LDA's graphical model and generative process, which represents documents as mixtures over latent topics and generates words probabilistically from topics. Variational inference is introduced as an approach for approximating the intractable posterior distribution over topics and learning model parameters.
QCONSF - ACID Is So Yesterday: Maintaining Data Consistency with SagasChris Richardson
This is a presentation I gave at QCONSF 2017
The services in a microservice architecture must be loosely coupled and so cannot share database tables. What’s more, two phase commit (a.k.a. a distributed transaction) is not a viable option for modern applications. Consequently, a microservices application must use the Saga pattern, which maintains data consistency using a series of local transactions.
In this presentation, you will learn how sagas work and how they differ from traditional transactions. We describe how to use sagas to develop business logic in a microservices application. You will learn effective techniques for orchestrating sagas and how to use messaging for reliability. We will describe the design of a saga framework for Java and show a sample application.
This is the slide of a session at the biggest tech conference in south Taiwan.
The session would introduce how to improve efficiency on the web application & database side.
This slide use Mandarin
The document discusses using a vector database to enable question answering with custom data. Key points:
- Data is converted to vector embeddings and stored in a vector database like Pinecone to allow for similarity searches.
- When a user asks a question, it is converted to a vector and queried against the database to retrieve similar content to provide as input to a language model for generating an answer.
- The OpenAI API can also be used to build an assistant using a language model, where custom data is loaded to enable answering questions about that data as a "support manager."
Introduction of basic building blocks in regular expressions example repetition token, anchor token, character token etc. Includes some challenges. (solutions included as well)
Announcing Amazon Athena - Instantly Analyze Your Data in S3 Using SQLAmazon Web Services
This document provides an overview and introduction to Amazon Athena, including:
- Athena is an interactive query service that allows users to analyze data directly from Amazon S3 using standard SQL.
- It is serverless, requiring no infrastructure management and with zero spin up time.
- Athena supports a variety of data formats and allows querying data directly from S3 without needing to load it elsewhere.
- Customers can use Athena to analyze large amounts of data in S3 in a cost effective and easy to use manner.
The document provides advice and resources for getting started with programming. It recommends finding a mentor and learning with friends to avoid discouragement. Several online resources are listed for learning programming fundamentals and competitive coding. The key is to try many things before giving up and keep learning new technologies to discover your interests. Starting with what interests you initially and keeping trying without giving up are advised.
The document describes PowerLyra, a system for differentiated graph computation and partitioning on skewed graphs. PowerLyra uses hybrid partitioning and computation strategies to balance locality and parallelism. The hybrid partitioning strategy, called Hybrid-cut, partitions low-degree vertices based on edges (like edge-cut) for locality, and partitions high-degree vertices based on vertices (like vertex-cut) for parallelism. The hybrid computation model processes high-degree vertices in a distributed manner for parallelism, and processes low-degree vertices locally for locality. PowerLyra also includes an optimization called zoning that groups vertices by type to improve data locality during communication.
Packaged enterprise apps are different from custom apps, and that means testing has to be different too. During this webinar we discussed these differences and provided strategies for supporting every aspect of implementing, testing, and running large enterprise applications.
Continuous representations of words and documents, which is recently referred to as Word Embeddings, have recently demonstrated large advancements in many of the Natural language processing tasks.
In this presentation we will provide an introduction to the most common methods of learning these representations. As well as previous methods in building these representations before the recent advances in deep learning, such as dimensionality reduction on the word co-occurrence matrix.
Moreover, we will present the continuous bag of word model (CBOW), one of the most successful models for word embeddings and one of the core models in word2vec, and in brief a glance of many other models of building representations for other tasks such as knowledge base embeddings.
Finally, we will motivate the potential of using such embeddings for many tasks that could be of importance for the group, such as semantic similarity, document clustering and retrieval.
This document discusses building a social network from scratch using microservices and Kubernetes. It outlines the technologies used in the DOF HUNT stack including Golang and Python for backends, Flutter for mobile, MySQL and MongoDB for databases, Elasticsearch for search, Redis for caching, NATS for messaging, and Linkerd for service mesh. It also covers monitoring and tracing with OpenCensus and Jaeger, and logging with Fluentd and Elasticsearch. Clean architecture is employed with modules separated into transport, handler, repository, and storage layers to allow easy transition between monolith and microservices.
Representation Learning of Vectors of Words and PhrasesFelipe Moraes
Talk about representation learning using word vectors such as Word2Vec, Paragraph Vector. Also introduced to neural network language models. Expose some applications using NNLM such as sentiment analysis and information retrieval.
Kotlin and Domain-Driven Design: A perfect match - Kotlin Meetup MunichFlorian Benz
Kotlin helps when pushing for Domain-Driven Design. A common complaint from Java developers is that wrapping and especially mapping values adds too much boilerplate. I rarely hear this from Kotlin developers. This talk presents some of our experience with Kotlin and Domain-Driven Design.
The following article was the basis for this talk:
https://dev.to/flbenz/kotlin-and-domain-driven-design-value-objects-4m32
Research on character level language modelling using LSTM for semi-supervised learning. The objective is learning inner layer representations of the language model for transfer learning into a classification one.
Generalizing NLP processes using Bi-directional LSTMs to learn character(byte) level embeddings of financial news headlines up too 8 bits ( 2**8 -1) in order to study the relationship between character vectors in financial news headlines in order to transfer learning in to classification models using UTF-8 encoding. Many traditional NLP steps (lemmatize, POS, NER, stemming...) are skipped when diving to byte level making the process more universal in terms of scope then task specific.
Configurações distribuídas com Spring Cloud ConfigEmmanuel Neri
Com as arquiteturas voltadas à microserviços um possível problema que possa vir a ocorrer durante o mantimento das aplicações é o gerenciamento de configurações, principalmente falando de aplicações Spring Boot onde temos grande parte das configurações baseados em arquivos, pode-se gerar um cenário repetitivo e desorganizado quando aplicado em várias aplicações. Com isso, essa palestra tem como objetivo apresentar uma solução para centralização dos arquivos de configuração em um serviço externo, possibilitando versionamento, reaproveitamento entre as aplicações, atualizações em runtime e criptografia nas propriedades de configurações utilizando o Spring Cloud Config.
This document provides an overview and instructions for installing and configuring ProxySQL. It discusses:
1. What ProxySQL is and its functions like load balancing and query caching
2. How to install ProxySQL on CentOS and configure the /etc/proxysql.cnf file
3. How to set up the ProxySQL schema to define servers, users, variables and other settings needed for operation
4. How to test ProxySQL functions like server status changes and benchmark performance
LVOUG meetup #4 - Case Study 10g to 11gMaris Elsins
My presentation on a case study of 10g to 11g upgrade at LVOUG meetup #4 in 2012. Includes preserving execution plans by exporting them from 10g and importing as SQL Plan Baselines in 11gR2
Presentation from SCALE 17x (https://www.socallinuxexpo.org/scale/17x/presentations/fast-http-string-processing-algorithms):
There are binary optimizations in HTTP/2, so the protocol becomes less about string processing. However, strings, sometimes quite large like URI or Cookie, stil exists in HTTP. A typical program working with HTTP, must perform various string operations, e.g. tokenization, string matching, searching for a pattern etc. Classic computer science describe many string processing algorithms, but HTTP strings are special and specialized algorithms can improve performance of the strings processing in several times.
This talk describes:
* How HTTP flood may make you HTTP parser the bottleneck x86-64 issues with branch mispredictions, caching and unaligned memory access
* C compiler optimizations for multi-branch statements and autovectorization
* switch-driven finite state machines (FSM) versus direct jumps (e.g. Ragel)
* what makes HTTP strings special and why LIBC functions aren't good
* strspn()- and strcasecmp()-like algorithms for HTTP strings using SSE and AVX
* efficient custom filtering to prevent injection attacks using AVX
* the cost of FPU context switch and how the Linux kernel works with SIMD
* all the topics are illustrated with microbenchmarks
DWX 2023 - Schnelles Feedback mit Pull-Request DeploymentsMarc Müller
Ein moderner DevOps Prozess wäre ohne Feature Branches und Pull-Request Workflows nicht mehr denkbar. Wie wird nun diese Änderung verifiziert? Oft schafft nur ein Deployment in eine isolierte PR Umgebung Klarheit. Somit sind Tests im Gesamtkontext mit Umsystemen und echter Datenpersistenz möglich, der Deployment und Upgrade Vorgang kann mitgetestet werden und nicht zuletzt kann auch ein Product Owner die Änderung an einem laufenden System anschauen.
This document discusses the use of Capistrano for deployment and system administration tasks. It provides an overview of Capistrano basics like defining roles and tasks. It demonstrates how to configure Capistrano to dump databases, check disk space, and deploy code to multiple servers. The document also covers common Capistrano commands, variables, deployment strategies, and creating a Capfile to get started with Capistrano.
Varnish is an HTTP accelerator that acts as a reverse proxy and cache. It is very fast due to being open source and outsourcing tasks to kernel functions. It relies on a massively multithreaded architecture that is partly event driven. It maps the cache store into memory using mmap and writes directly from mapped memory for maximum performance. Logging includes all request headers. Wikia uses Varnish across 4 datacenters with rapid cache invalidations and a RabbitMQ queue to handle invalidations. SSDs and tuning help optimize performance.
This document summarizes various SQL tracing methods in Oracle using the command line, including:
1) Tracing your own session using SQL_TRACE, DBMS_SESSION.SET_SQL_TRACE, or DBMS_SESSION.SESSION_TRACE_ENABLE.
2) Using DBMS_APPLICATION_INFO to set client identifiers for tracing.
3) Tracing another session using DBMS_MONITOR procedures or client identifiers.
4) Tracing a specific process or SQL statement using ALTER SYSTEM events.
5) Identifying trace files and explicitly setting the trace file name.
Looks at the challenge and opportunity of trying to adopt the JAMstack ("static app") model in a large enterprise based on the experience of PayPal. Talk was given at QCon London 2019.
C15LV: Ins and Outs of Concurrent Processing Configuration in Oracle e-Busine...Maris Elsins
Concurrent processing is a critical functionality in any e-Business Suite system. DBAs will recognize problems like jammed concurrent manager queues, failover problems, load balancing issues causing one node to be more loaded that another, performance overhead or even an unwanted bounce of the managers when incompatibility rules change. It is important to understand the configuration of a reliable concurrent processing environment, therefore, topics like PCP, node affinity, load balancing, optimal cache size and sleep time settings, separation of manager duties, request groups and others will be discussed.
Step by Step Personal Drive to One Drive Migration using SPMTIT Industry
The document provides step-by-step instructions for migrating files from Personal Drives to OneDrive using the SharePoint Migration Tool (SPMT). It details restoring files to a common location, installing SPMT, configuring variables and tasks, running parallel migrations via PowerShell, monitoring performance, and analyzing the migration reports and logs generated upon completion to understand failures, successes, and statistics. Email notifications are also triggered containing personalized migration summaries and lists of skipped files for each user.
Профилирование распределенных систем, Александр Казаков, СКБ Контур it-people
The document contains log entries from a distributed system on March 23, 2016. It logs information about various services, components and requests. Some key details include:
- It logs the start and completion of asynchronous tasks across multiple services including "PageAsyncTask" and "ConcurrentAsyncTaskManager".
- It logs successful requests to a "ZebraSearch" component with response times between 0-9 seconds for some requests.
- Other log lines provide details on the response size, remote server IP addresses, and cache updates for file downloads.
This document discusses using window functions in PostgreSQL to analyze sales data. It shows a table with movie titles, categories, and total sales. Window functions can be used to calculate things like running totals, ranks, and more to analyze the sales data across rows.
BASTA Spring 2023 - SCHNELLES FEEDBACK MIT PULL REQUEST DEPLOYMENTSMarc Müller
Ein moderner DevOps-Prozess wäre ohne Feature Branches und Pull-Request-Workflows nicht mehr denkbar. Wie wird nun diese Änderung verifiziert? Oft schafft nur ein Deployment in eine isolierte PR-Umgebung Klarheit. Somit sind Tests im Gesamtkontext mit Umsystemen und echter Datenpersistenz möglich, der Deployment- und Upgrade-Vorgang kann mitgetestet werden und nicht zuletzt kann auch ein Product Owner die Änderung an einem laufenden System anschauen. Dieser Vortrag erklärt, was für ein Pull Request Deployment alles nötig ist und zeigt auch Ansätze für Herausforderungen wie Datenbankschema-Deployment und Dateninitialisierung. Die Beispiele werden anhand von Azure DevOps und Azure Kubernetes Services vorgestellt.
Things like Infrastructure as Code, Service Discovery and Config Management can and have helped us to quickly build and rebuild infrastructure but we haven't nearly spend enough time to train our self to review, monitor and respond to outages. Does our platform degrade in a graceful way or what does a high cpu load really mean? What can we learn from level 1 outages to be able to run our platforms more reliably.
We all love infrastructure as code, we automate everything ™. However making sure all of our infrastructure assets are monitored effectively can be slow and resource intensive multi stage process. During this talk we will investigate how we can setup nomad cluster that can automatically scale our infrastructure both horizontally as vertically to be able to cope with increased demand by users/
This talk will focus on making sure we on configuring Nomad and its new autoscaler component to be able to make data driven decisions about scaling nomad jobs in or out to fit current customers usage.
Cloudera Enabling Native Integration of NoSQL HBase with Cloud Providers.pdfwchevreuil
This document discusses enabling native integration of the NoSQL database HBase with cloud providers. It covers topics like reducing total cost of ownership through automatic scaling and storage optimizations, security simplification using JWT instead of Kerberos, high availability through techniques like multi-availability zones, and benchmark results comparing performance of HBase on cloud storage like S3 versus on-premises block storage. Benchmark results showed higher throughput and lower latencies for most workloads when using cloud storage compared to on-premises block storage. A cost case study also showed monthly costs could be 26.8% lower using cloud storage with ephemeral caches versus block storage for a 50TB read/write workload on AWS.
1. The HDFS client write flow involves the client calling DistributedFileSystem.create() to create a file, which performs an RPC call to the namenode to add the file. A DFSOutputStream is created and a DataStreamer thread is started.
2. The client writes data by filling buffers that are flushed and grouped into packets. Packets are enqueued for asynchronous processing by the DataStreamer thread.
3. The DataStreamer reads packets and writes data to datanodes, which write to local disk and mirrors. If the last packet, a finalize block call is made to the namenode.
This is a copy of the NoSQL Day 2019 session presented in Washington D.C on May 2019. It covers a series of the most common HBase issues observed among Cloudera customer base, together with RCA and recipes for recovery.
Within Hbase 2, a new fix tool has been developed with helpful methods for fixing issues within the new AssignmentManager and other additional useful operation. This is a copy of the presentation from HBaseCon Asia 2019 held in Beijing during summer of 2019.
WebHDFS x HttpFS are common source of confusion. This slideset highlights differences and similarities between these two Web interfaces for accessing an HDFS cluster.
Overview of HBase cluster replication feature, covering implementation details as well as monitoring tools and tips for troubleshooting and support of Replication deployments.
This document discusses tuning MapReduce jobs and HBase configurations for a music trending application at Nokia. It provides details on profiling and tuning two MapReduce jobs - Tweets counting and entity ID search. Tuning efforts included applying combiners, optimizing HBase scans, removing unnecessary code, and increasing cache usage. Performance improved from 46 minutes to 20 minutes for Tweets counting and from 1 hour 10 minutes to 6 minutes for entity search after tuning. Refactoring ideas are also proposed like performing entity lookups in a standalone process instead of MapReduce.
O documento discute o surgimento de bancos de dados NoSQL para lidar com grandes volumes de dados (Big Data). Apresenta as limitações dos bancos de dados relacionais tradicionais para armazenar e processar Big Data e introduz os principais modelos e sistemas de gerenciamento de bancos de dados NoSQL, como MongoDB, Cassandra, Voldemort e Redis. Não existe uma solução "bala de prata" e a escolha depende do cenário e requisitos de cada aplicação.
O documento apresenta uma introdução ao Hadoop, incluindo seu surgimento, componentes e funcionalidades. Resume os principais tópicos do Big Data, Hadoop, HDFS, MapReduce e o ecossistema Hadoop.
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsPeter Muessig
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13. •
•
2017-04-12 09:05:46,518 INFO org.apache.hadoop.hbase.master.RegionStates: Transition {a3a747cf03c1249c9d394ec7114b4c17 state=OFFLINE, ts=1492013146322,
server=host-10-17-81-106.coe.cloudera.com,60020,1491568321924} to {a3a747cf03c1249c9d394ec7114b4c17 state=PENDING_OPEN, ts=1492013146518,
server=host-10-17-81-106.coe.cloudera.com,60020,1492013113599}
017-04-12 09:05:48,169 INFO org.apache.hadoop.hbase.master.RegionStates: Transition {a3a747cf03c1249c9d394ec7114b4c17 state=PENDING_OPEN, ts=1492013146518,
server=host-10-17-81-106.coe.cloudera.com,60020,1492013113599} to {a3a747cf03c1249c9d394ec7114b4c17 state=OPENING, ts=1492013148169,
server=host-10-17-81-106.coe.cloudera.com,60020,1492013113599}
2017-04-12 09:05:48,920 INFO org.apache.hadoop.hbase.master.RegionStates: Transition {a3a747cf03c1249c9d394ec7114b4c17 state=OPENING, ts=1492013148169,
server=host-10-17-81-106.coe.cloudera.com,60020,1492013113599} to {a3a747cf03c1249c9d394ec7114b4c17 state=OPEN, ts=1492013148920,
server=host-10-17-81-106.coe.cloudera.com,60020,1492013113599}
2017-04-12 09:05:46,749 INFO org.apache.hadoop.hbase.regionserver.RSRpcServices: Open test,,1488813550988.a3a747cf03c1249c9d394ec7114b4c17.
2017-04-12 09:05:48,529 INFO org.apache.hadoop.hbase.regionserver.HRegion: Onlined a3a747cf03c1249c9d394ec7114b4c17; next sequenceid=1734
2017-04-12 09:05:48,534 INFO org.apache.hadoop.hbase.regionserver.HRegionServer: Post open deploy tasks for test,,1488813550988.a3a747cf03c1249c9d394ec7114b4c17.
2017-04-12 09:05:48,787 INFO org.apache.hadoop.hbase.MetaTableAccessor: Updated row test,,1488813550988.a3a747cf03c1249c9d394ec7114b4c17. with
server=host-10-17-81-106.coe.cloudera.com,60020,1492013113599
14. •
•
2017-04-12 09:05:46,313 INFO org.apache.hadoop.hbase.master.AssignmentManager: Assigning 3 region(s) to host-10-17-81-106.coe.cloudera.com,60020,1492013113599
2017-04-12 09:05:46,518 INFO org.apache.hadoop.hbase.master.RegionStates: Transition {752c827664cedb16bd3b1b7ecbda25b7 state=OFFLINE, ts=1492013146322,
server=host-10-17-81-106.coe.cloudera.com,60020,1491568321924} to {752c827664cedb16bd3b1b7ecbda25b7 state=PENDING_OPEN, ts=1492013146518,
server=host-10-17-81-106.coe.cloudera.com,60020,1492013113599}
2017-04-12 09:05:46,819 INFO org.apache.hadoop.hbase.master.RegionStates: Transition {752c827664cedb16bd3b1b7ecbda25b7 state=PENDING_OPEN, ts=1492013146518,
server=host-10-17-81-106.coe.cloudera.com,60020,1492013113599} to {752c827664cedb16bd3b1b7ecbda25b7 state=OPENING, ts=1492013146819,
server=host-10-17-81-106.coe.cloudera.com,60020,1492013113599}
2017-04-12 09:05:47,998 INFO org.apache.hadoop.hbase.master.RegionStates: Transition {752c827664cedb16bd3b1b7ecbda25b7 state=OPENING, ts=1492013146819,
server=host-10-17-81-106.coe.cloudera.com,60020,1492013113599} to {752c827664cedb16bd3b1b7ecbda25b7 state=CLOSED, ts=1492013147998,
server=host-10-17-81-106.coe.cloudera.com,60020,1492013113599}
2017-04-12 09:05:48,006 INFO org.apache.hadoop.hbase.master.RegionStates: Transition {752c827664cedb16bd3b1b7ecbda25b7 state=CLOSED, ts=1492013148004,
server=host-10-17-81-106.coe.cloudera.com,60020,1492013113599} to {752c827664cedb16bd3b1b7ecbda25b7 state=OFFLINE, ts=1492013148006,
server=host-10-17-81-106.coe.cloudera.com,60020,1492013113599}
2017-04-12 09:05:46,556 INFO org.apache.hadoop.hbase.regionserver.RSRpcServices: Open test3-clone2,,1491211108318.752c827664cedb16bd3b1b7ecbda25b7.
2017-04-12 09:05:47,881 ERROR org.apache.hadoop.hbase.regionserver.HRegion: Could not initialize all stores for the region=test3-clone2,,1491211108318.752c827664cedb16bd3b1b7ecbda25b7.
2017-04-12 09:05:47,902 ERROR org.apache.hadoop.hbase.regionserver.handler.OpenRegionHandler: Failed open of region=test3-clone2,,1491211108318.752c827664cedb16bd3b1b7ecbda25b7.,
starting to roll back the global memstore size.
java.io.IOException: java.io.IOException: java.io.FileNotFoundException: Unable to open link: org.apache.hadoop.hbase.io.HFileLink
locations=[hdfs://nameservice1/hbase/data/default/test3/99e0261dda05c9f5ed7a803201f030e8/t/de84a1ab669346d381057e0903df9186,
hdfs://nameservice1/hbase/.tmp/data/default/test3/99e0261dda05c9f5ed7a803201f030e8/t/de84a1ab669346d381057e0903df9186,
hdfs://nameservice1/hbase/mobdir/data/default/test3/99e0261dda05c9f5ed7a803201f030e8/t/de84a1ab669346d381057e0903df9186,
hdfs://nameservice1/hbase/archive/data/default/test3/99e0261dda05c9f5ed7a803201f030e8/t/de84a1ab669346d381057e0903df9186]
at org.apache.hadoop.hbase.regionserver.HRegion.initializeStores(HRegion.java:949)
at org.apache.hadoop.hbase.regionserver.HRegion.initializeRegionInternals(HRegion.java:824)
at org.apache.hadoop.hbase.regionserver.HRegion.initialize(HRegion.java:799)
at org.apache.hadoop.hbase.regionserver.HRegion.openHRegion(HRegion.java:6480)
at org.apache.hadoop.hbase.regionserver.HRegion.openHRegion(HRegion.java:6441)
at org.apache.hadoop.hbase.regionserver.HRegion.openHRegion(HRegion.java:6412)
at org.apache.hadoop.hbase.regionserver.HRegion.openHRegion(HRegion.java:6368)