SymmetricDS é um software open source para replicação multi-master de bancos de dados. Ele fornece recursos como filtragem de dados, transformação de dados e resolução de conflitos. Sua arquitetura permite a replicação de dados entre vários bancos de dados de forma assíncrona e eficiente. O documento descreve como configurar e implantar o SymmetricDS para replicar dados entre um banco de dados central e filiais.
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Check the other Lectures and courses in
http://Linux4EnbeddedSystems.com
or Follow our Facebook Group at
- Facebook: @LinuxforEmbeddedSystems
Lecturer Profile:
- https://www.linkedin.com/in/ahmedelarabawy
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Microsoft SQL Server internals & architectureKevin Kline
From noted SQL Server expert and author Kevin Kline - Let’s face it. You can effectively do many IT jobs related to Microsoft SQL Server without knowing the internals of how SQL Server works. Many great developers, DBAs, and designers get their day-to-day work completed on time and with reasonable quality while never really knowing what’s happening behind the scenes. But if you want to take your skills to the next level, it’s critical to know SQL Server’s internal processes and architecture. This session will answer questions like:
- What are the various areas of memory inside of SQL Server?
- How are queries handled behind the scenes?
- What does SQL Server do with procedural code, like functions, procedures, and triggers?
- What happens during checkpoints? Lazywrites?
- How are IOs handled with regards to transaction logs and database?
- What happens when transaction logs and databases grow or shrinks?
This fast paced session will take you through many aspects of the internal operations of SQL Server and, for those topics we don’t cover, will point you to resources where you can get more information.
Is it easier to add functional programming features to a query language, or to add query capabilities to a functional language? In Morel, we have done the latter.
Functional and query languages have much in common, and yet much to learn from each other. Functional languages have a rich type system that includes polymorphism and functions-as-values and Turing-complete expressiveness; query languages have optimization techniques that can make programs several orders of magnitude faster, and runtimes that can use thousands of nodes to execute queries over terabytes of data.
Morel is an implementation of Standard ML on the JVM, with language extensions to allow relational expressions. Its compiler can translate programs to relational algebra and, via Apache Calcite’s query optimizer, run those programs on relational backends.
In this talk, we describe the principles that drove Morel’s design, the problems that we had to solve in order to implement a hybrid functional/relational language, and how Morel can be applied to implement data-intensive systems.
(A talk given by Julian Hyde at Strange Loop 2021, St. Louis, MO, on October 1st, 2021.)
Course 102: Lecture 3: Basic Concepts And Commands Ahmed El-Arabawy
This lecture covers the basic file management commands
Check the other Lectures and courses in
http://Linux4EnbeddedSystems.com
or Follow our Facebook Group at
- Facebook: @LinuxforEmbeddedSystems
Lecturer Profile:
- https://www.linkedin.com/in/ahmedelarabawy
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Hadoop is a well-known framework used for big data processing now-a-days. It implements MapReduce for processing and utilizes distributed file system known as Hadoop Distributed File System (HDFS) to store data. HDFS provides fault tolerant, distributed and scalable storage for big data so that MapReduce can easily perform jobs on this data. Knowledge and understanding of data storage over HDFS is very important for a researcher working on Hadoop for big data storage and processing optimization. The aim of this presentation is to describe the architecture and process flow of HDFS. This presentation highlights prominent features of this file system implemented by Hadoop to execute MapReduce jobs. Moreover the presentation provides the description of process flow for achieving the design objectives of HDFS. Future research directions to explore and improve HDFS performance are also elaborated on.
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Présentation effectuée pendant la piscine du POC Innovation de Epitech (15 février 2022) par Christophe Villeneuve avec Sébastien "MariaDB : une base de données NewSQL".
Après un tour d'horizon de l'évolution des bases de données, nous nous intéresserons sur la base de données MariaDB qui propose le NewSQL (NoSQL + SQL) et ces différents storages engines disponibles. Ainsi chaque problématique projet peut être couverte par celle-ci.
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PostgreSQL databases use the Write-Ahead Logging approach for the replication of data. At the same time, customers worldwide have asked for Oracle GoldenGate to support replication to and from PostgreSQL databases. The wait is over! This session will introduce Oracle GoldenGate for PostgreSQL and highlight what needs to be looked at to ensure successful replication for any PostgreSQL environment.
Webinar | How to Understand Apache Cassandra™ Performance Through Read/Writ...DataStax
In this webinar, you will leverage free and open source tools as well as enterprise-grade utilities developed by DataStax to get a solid grasp on the performance of a masterless distributed database like Cassandra. You’ll also get the opportunity to walk through DataStax Enterprise Insights dashboards and see exactly how to identify performance bottlenecks.
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This ppt helps people who would like to present their industrial training presentation on Oracle 11g DBA.
This one includes all the operations that dba has to be perform and some other internal concepts of Oracle.
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Let's take a look at:
PostgreSQL and buzz it has created
Architecture
Oracle Compatibility
Performance Feature
Security Features
High Availability Features
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User Stories
What’s coming up in v9.3
How to start adopting
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MySQL Workbench, MySQL Enterprise Monitor, 또는 sys schema에서 제공하는 성능 리포트를 이용하여 최적화가 필요한 쿼리를 어떻게 찾는지 배워봅시다.
A quick comparison of Hadoop and Apache Spark with a detailed introduction.
Hadoop and Apache Spark are both big-data frameworks, but they don't really serve the same purposes. They do different things.
Looking for Similar IT Services?
Write to us business@altencalsoftlabs.com
(OR)
Visit Us @ https://www.altencalsoftlabs.com/
Hadoop is a well-known framework used for big data processing now-a-days. It implements MapReduce for processing and utilizes distributed file system known as Hadoop Distributed File System (HDFS) to store data. HDFS provides fault tolerant, distributed and scalable storage for big data so that MapReduce can easily perform jobs on this data. Knowledge and understanding of data storage over HDFS is very important for a researcher working on Hadoop for big data storage and processing optimization. The aim of this presentation is to describe the architecture and process flow of HDFS. This presentation highlights prominent features of this file system implemented by Hadoop to execute MapReduce jobs. Moreover the presentation provides the description of process flow for achieving the design objectives of HDFS. Future research directions to explore and improve HDFS performance are also elaborated on.
O AWS Database Migration Service ajuda você a migrar bancos de dados para a AWS de modo fácil e seguro. O banco de dados de origem permanece totalmente operacional durante a migração, minimizando períodos de inatividade para aplicativos que dependem do banco de dados.
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2. Agenda
Replicação Multi-master
Casos de uso x Soluções Internas
SymmetricDS
Recursos
Requisitos
Arquitetura
Instalação
Con guração da Replicação
Con guração da Transformação
Con guração de Filtros
Solução de Problemas
3. Replicação Multi-master
É um método de replicação de banco de dados que permite que os dados sejam
armazenados em um grupo de computadores, e atualizados por qualquer membro do
grupo.
Todos os membros do grupo gravam e respondem a consultas.
É responsável por propagar as alterações de dados feitas por cada membro para o grupo e
resolver con itos que possam surgir entre as alterações simultâneas feitas por diferentes
membros.
Wikipedia: Multi-master replication(https://en.wikipedia.org/wiki/Multi-master_replication)
4. Casos de uso - Internos
Replicação de Banco de Dados
produtos da empresa
ERP cliente x cloud x departamentos de serviços
de clientes entre liais
matriz x filiais x depósitos x centrais de distribuição, etc
sistemas internos
atendimentos para banco de dados de análises de negócios e métricas
novos projetos
cloud x clientes
5. O que tinhamos?
Replicadores desenvolvidos para cada projeto
Manutenção constante
Falta de padronização
Evolução limitada
Replicador não é um produto, é uma ferramenta
6. SymmetricDS é um software open source para replicação de banco de dados e arquivos,
com suporte a replicação de uma via, multi-master, sincronização ltrada e transformações.
Projetado para suportar um grande número de banco de dados, trabalhar em redes de
baixa taxa de transferência e suportar períodos de interrupção de rede.
Licença GPL version 3.0
www.symmetricds.org(https://www.symmetricds.org)
www.jumpmind.com/products/symmetricds(http://www.jumpmind.com/products/symmetricds)
7. SymmetricDS - Principais Recursos
Replicação de Banco de Dados
Capturar de dados alterados e replicar em tempo quase real - Change Data Capture
(CDC).
Filtragem vertical e horizontal de partes de uma tabela.
Transformação para conversão, melhoria e ltro de dados.
Mapear colunas e tabelas para ligar diferentes aplicações.
Detecta con itos e resolve usando regras automatizadas ou noti cação para intervenção
manual.
Suporte a Transações
Criação de Banco de dados Automática
Versionamento de Banco de Dados
8. SymmetricDS - Principais Recursos
Con guração
Con guração Centralizada
Sincronização Uni-direcional, bi-direcional e multi-master
Sincronização agendada ou contínua em tempo quase real.
Sincronização organizada em grupos.
Alterações agrupadas em lotes para melhor rastreamento e e ciência.
Carga inicial de dados para preparar o banco de dados e sistema de arquivos remoto
para a sincronização das alterações.
9. SymmetricDS - Principais Recursos
Comunicação
Protocolo e ciente para operação em redes de baixa taxa de transferência.
Criptogra a na transferência para comunicação segura.
Recuperação automatica após indisponibilidade e quedas de rede.
Garantia de Entrega
Canais Independentes de Sincronização
10. SymmetricDS - Principais Recursos
Mobile
Android - biblioteca Java.
iOS - biblioteca C.
Syncronização bidirecional do SQLite para qualquer banco de dados.
Funciona no modo O ine e sincroniza automaticamente quando a rede está disponível.
11. SymmetricDS - Principais Recursos
Sincronização de Arquivos
API Extensível
Instalação: Aplicativo, Serviço, Aplicação Web e Componente Embutido
Pode ser Embutido em outra Aplicação - PDV
Gerenciamento Remoto - Console JMX ou REST
12. SymmetricDS - Banco de dados Suportados
Mais de 20 diferentes bancos de dados suportados
Derby, DB2, Firebird, Greenplum, H2, HSQLDB, Informix, Interbase, MariaDB, MongoDB,
MySQL, Oracle,PostgreSQL, Redshift, SQL Anywhere, SQL Azure, SQL Server, SQLite, Sybase
13. SymmetricDS - Pré Requisitos
Java SE Runtime 7 ou 8
Sistemas Operacionais
Linux, Windows, Unix, Mac OS X, Android, iOS e Windows Mobile
Servidores de Aplicação
Tomcat, JBoss, Jetty, WebLogic, WebSphere
www.jumpmind.com/products/symmetricds/requirements(http://www.jumpmind.com/products/symmetricds/requirements)
15. SymmetricDS - Arquitetura
Con guração no banco de dados Central em tabelas do SymmetricDS.
As tabelas con guradas para replicação são examinadas e são criadas triggers para
gravar as alterações, a replicação de dados contém os valores antes e depois das
alterações e são armazenados no formato CSV.
Após as alterações serem capturadas, o destino será de nido a partir das rotas
con guradas, os dados são extraídos no formato CSV e transformados, quando
necessário, então é criado um arquivo com o lote, antes de ser enviado para o destino
através de http.
Quando o lote é recebido no destino, ele será carregado e aplicado no banco de dados.
17. SymmetricDS - Instalação
Con gurar node Central na pasta engines
"engines/central-1.properties"
external.id=1
engine.name=central-1
sync.url=http://138.204.120.35:31415/sync/central-1
group.id=grupo_central
db.url=jdbc:postgresql://127.0.0.1/database?protocolVersion=2&socketTimeout=300&tcpKeepAlive=true
db.driver=org.postgresql.Driver
db.user=username
db.password=enc:encripted_password
registration.url=
db.validation.query=select 1
db.init.sql=
db.connection.properties=
Instalar e Iniciar Serviço - as tabelas do SymmetricDS são criadas
Executar o SQL de con guração da replicação
18. SymmetricDS - Con gurar Replicação
Con guração Padrão
INSERT INTO sym_node_group (node_group_id) VALUES ('grupo_central');
INSERT INTO sym_node_group (node_group_id) VALUES ('grupo_loja');
-- grupo_loja envia e busca alterações
INSERT INTO sym_node_group_link (source_node_group_id, target_node_group_id, data_event_action)
VALUES ('grupo_loja', 'grupo_central', 'P');
-- grupo_central aguarda conexões do grupo_loja e recebe e disponbiliza alterações
INSERT INTO sym_node_group_link (source_node_group_id, target_node_group_id, data_event_action)
VALUES ('grupo_central', 'grupo_loja', 'W');
INSERT INTO sym_router (router_id, source_node_group_id, target_node_group_id, create_time, last_update_time)
VALUES ('grupo_central-grupo_loja-w', 'grupo_central', 'grupo_loja', current_timestamp, current_timestamp
INSERT INTO sym_router (router_id, source_node_group_id, target_node_group_id, create_time, last_update_time)
VALUES ('grupo_loja-grupo_central-p', 'grupo_loja', 'grupo_central', current_timestamp, current_timestamp
20. Con gurar Transformação - Tabelas
Transformação de uma para multiplas tabelas
Especi car a direção, quando executar e quais tabelas são envolvidas
INSERT INTO sym_transform_table
(transform_id, source_node_group_id, target_node_group_id, transform_point,
source_table_name, target_table_name, delete_action, column_policy)
VALUES
('clientes_para_clientes_importados', 'grupo_loja', 'grupo_central', 'EXTRACT',
'clientes', 'clientes_importados', 'NONE', 'SPECIFIED'),
('customers_to_customers', 'grupo_loja', 'grupo_central', 'EXTRACT',
'clientes', 'clientes', 'NONE', 'IMPLIED');
21. Con gurar Transformação - Colunas
Especi car as colunas envolvidas e o tipo da transformação
INSERT INTO sym_transform_column
(transform_id, include_on, source_column_name, target_column_name, pk, transform_type)
VALUES
('clientes_para_clientes_importados', '*', 'id', 'id_cliente', 1, 'copy'),
('clientes_para_clientes_importados', '*', 'nome', 'nome_cliente', 1, 'copy'),
('clientes', '*', 'id', 'id', 0, 'copy');
22. Con gurar Filtros
Antes de gravar
Após gravar
Após gravar o Lote
Após commit do lote
Roolback do lote
Quando ocorre um Erro
insert into sym_load_filter (...)
values
('SampleFilter','BSH','Client','Server',NULL,NULL, 'ITEM_SELLING_PRICE', 1, 1, 1,'
if (OLD_COST > COST) {
return false
} else {
return true
}
', null,null,null,null,null, current_timestamp,'Documentation',current_timestamp,1,1);
23. SymmetricDS - Instalação Filial
Instalar e Con gurar o SymmetricDS na Filial
"engines/loja-2.properties"
external.id=2
engine.name=loja-2
sync.url=http://localhost:31415/sync/loja-2
group.id=grupo_loja
db.url=jdbc:postgresql://127.0.0.1/database?protocolVersion=2&socketTimeout=300&tcpKeepAlive=true
db.driver=org.postgresql.Driver
db.user=username
db.password=enc:encripted_password
registration.url=http://138.204.120.30:31415/sync/central-1
db.validation.query=select 1
db.init.sql=
db.connection.properties=
Instalar e Iniciar o Serviço
24. SymmetricDS - Diretórios e Arquivos
conf: para con gurações detalhadas do comportamento do SymmetricDS
engines: cada arquivo corresponde a um node, e a um único banco de dados
logs: contém os logs da replicação e erros ocorridos
tmp: arquivos que serão enviados ou que foram recebidos
25. SymmetricDS - Resolução de problemas
Como identi car erros no Envio
Documentação: "Outgoing Batch Errors"(http://www.symmetricds.org/doc/3.8/html/user-guide.html#_outgoing_batch_errors)
-- lista a fila de pacotes do replicador que estão com erro
SELECT * FROM sym_outgoing_batch
WHERE error_flag = 1;
-- conta o número de pacotes pendentes, pode ter com ou sem erros
SELECT COUNT(*) FROM sym_outgoing_batch
WHERE status != 'OK';
-- lista registros do sistema pendentes em pacotes com erro
SELECT * FROM sym_data
WHERE data_id IN (
SELECT data_id FROM sym_data_event
WHERE batch_id IN (
SELECT batch_id FROM sym_outgoing_batch
WHERE error_flag = 1));
26. SymmetricDS - Resolução de problemas
Tabela de Erros
Erros no recebimento cam na tabela sym_incoming_error
27. Thank you
Cesar Romero
Software Architect, Trier Sistemas, Tubarão - SC
cesarliws@gmail.com(mailto:cesarliws@gmail.com)
http://blog.cesarromero.com.br/(http://blog.cesarromero.com.br/)
@cesarliws(http://twitter.com/cesarliws)