Presentation on how to move from the Alfresco Search Services product based in Apache Solr to the new Alfresco Search Enterprise integrated with Elasticsearch and Amazon Opensearch.
This document summarizes a presentation about Alfresco Search Services 2.0. Key points include:
- Solr was updated to remove the custom content store and leverage more built-in Solr features like replication and backups. This improved performance and reduced disk usage.
- New date fields were added that break dates down into individual components like year, month, day, etc. to enable more granular search queries.
- Asynchronous maintenance actions were introduced to schedule and retry tasks like reindexing, purging, and fixing index issues in the background.
- Security was enhanced with support for mutual TLS and storing passwords in JVM properties instead of plain text files. Performance tracking and indexing controls
The document provides an overview and best practices for tuning an Alfresco installation. It discusses disabling unused services, limiting group hierarchies, monitoring resources, optimizing Solr configuration, indexing processes, and query caching. General tips include separating custom configurations, testing backups and changes, and using support tools for troubleshooting performance issues.
Alfresco DevCon 2019 (Edinburgh)
"Transforming the Transformers" for Alfresco Content Services (ACS) 6.1 & beyond
https://community.alfresco.com/community/ecm/blog/2019/02/07/alfresco-transform-service-new-with-acs-61
Alfresco provides various content transformation options across the Digital Business Platform (DBP). In this talk, we will explore the new independently-scalable Alfresco Transform Service. This enables a new option for transforms to be asynchronously off-loaded by Alfresco Content Services (ACS).
https://devcon.alfresco.com/speaker/jan-vonka/
This session will provide a guide to Alfresco truststores and keystores. Several live examples will be shown, including the replacement of existing cryptographic stores or certificates. Additionally, a troubleshooting configuration guide for mTLS communication will be provided.
The objective of this article is to describe what to monitor in and around Alfresco in order to have a good understanding of how the applications are performing and to be aware of potential issues.
Features of Alfresco Search Services.
Features of Alfresco Search & Insight Engine.
Future plans for the product
---
DEMO GUIDE
[1] Queries: Share > Node Browser
ASPECT:'cm:titled' AND cm:title:'*Sample*' AND TEXT:'code'
SELECT * FROM cm:titled WHERE cm:title like '%Sample%' AND CONTAINS('code')
[2] Queries: Share > JS Console
var ctxt = Packages.org.springframework.web.context.ContextLoader.getCurrentWebApplicationContext();
var searchService = ctxt.getBean('SearchService', org.alfresco.service.cmr.search.SearchService);
var StoreRef = Packages.org.alfresco.service.cmr.repository.StoreRef;
var SearchService = Packages.org.alfresco.service.cmr.search.SearchService;
var ResultSet = Packages.org.alfresco.repo.search.impl.lucene.SolrJSONResultSet;
ResultSet =
searchService.query(
StoreRef.STORE_REF_WORKSPACE_SPACESSTORE,
SearchService.LANGUAGE_FTS_ALFRESCO,
"ASPECT:'cm:titled' AND cm:title:'*Sample*' AND TEXT:'code'");
logger.log(ResultSet.getNodeRefs());
---
var ctxt = Packages.org.springframework.web.context.ContextLoader.getCurrentWebApplicationContext();
var searchService = ctxt.getBean('SearchService', org.alfresco.service.cmr.search.SearchService);
var StoreRef = Packages.org.alfresco.service.cmr.repository.StoreRef;
var SearchService = Packages.org.alfresco.service.cmr.search.SearchService;
var ResultSet = Packages.org.alfresco.repo.search.impl.lucene.SolrJSONResultSet;
ResultSet =
searchService.query(
StoreRef.STORE_REF_WORKSPACE_SPACESSTORE,
SearchService.LANGUAGE_CMIS_ALFRESCO,
"SELECT * FROM cm:titled WHERE cm:title like '%Sample%' AND CONTAINS('code')");
logger.log(ResultSet.getNodeRefs());
---
var def =
{
query: "ASPECT:'cm:titled' AND cm:title:'*Sample*' AND TEXT:'code'",
language: "fts-alfresco"
};
var results = search.query(def);
logger.log(results);
[3] Queries: api-explorer
{
"query": {
"language": "afts",
"query": "ASPECT:\"cm:titled\" AND cm:title:\"*Sample\" AND TEXT:\"code\""
}
}
---
{
"query": {
"language": "cmis",
"query": "SELECT * FROM cm:titled WHERE cm:title like '%Sample%' AND CONTAINS('code')"
}
}
[4] Queries: CMIS Workbench > Groovy Console
rs = session.query("SELECT * FROM cm:titled WHERE cm:title like '%Sample%' AND CONTAINS('code')", false)
for (res in rs) {
println(res.getPropertyValueById('cmis:objectId'))
}
[5] Queries: SOLR Web Console > (alfresco) > Query
/afts
ASPECT:'cm:titled' AND cm:title:'*Sample*' AND TEXT:'code'
---
/cmis
SELECT * FROM cm:titled WHERE cm:title like '%Sample%' AND CONTAINS('code')
---
This is the session delivered during the Alfresco Developers Conference in Lisbon, January 2018. Learn all what you need to know to perform a proper backup and disaster recovery strategy. From a single server installation with hundreds of documents to a large deployment with multiple nodes, layers, databases and multi-million documents. What is the best way for each case?
The document discusses performance tuning of Alfresco. It covers JVM tuning including memory and garbage collection settings. It also discusses analyzing garbage collection logs and common problems. The document outlines different cache mechanisms in Alfresco including L1, L2 caches and Hazelcast caching. Tuning caches based on data change frequency and hit ratios is recommended. Finally, the document provides guidance on investigating performance issues by examining logs, threads, databases, storage and Alfresco/Solr configurations and settings.
This document summarizes a presentation about Alfresco Search Services 2.0. Key points include:
- Solr was updated to remove the custom content store and leverage more built-in Solr features like replication and backups. This improved performance and reduced disk usage.
- New date fields were added that break dates down into individual components like year, month, day, etc. to enable more granular search queries.
- Asynchronous maintenance actions were introduced to schedule and retry tasks like reindexing, purging, and fixing index issues in the background.
- Security was enhanced with support for mutual TLS and storing passwords in JVM properties instead of plain text files. Performance tracking and indexing controls
The document provides an overview and best practices for tuning an Alfresco installation. It discusses disabling unused services, limiting group hierarchies, monitoring resources, optimizing Solr configuration, indexing processes, and query caching. General tips include separating custom configurations, testing backups and changes, and using support tools for troubleshooting performance issues.
Alfresco DevCon 2019 (Edinburgh)
"Transforming the Transformers" for Alfresco Content Services (ACS) 6.1 & beyond
https://community.alfresco.com/community/ecm/blog/2019/02/07/alfresco-transform-service-new-with-acs-61
Alfresco provides various content transformation options across the Digital Business Platform (DBP). In this talk, we will explore the new independently-scalable Alfresco Transform Service. This enables a new option for transforms to be asynchronously off-loaded by Alfresco Content Services (ACS).
https://devcon.alfresco.com/speaker/jan-vonka/
This session will provide a guide to Alfresco truststores and keystores. Several live examples will be shown, including the replacement of existing cryptographic stores or certificates. Additionally, a troubleshooting configuration guide for mTLS communication will be provided.
The objective of this article is to describe what to monitor in and around Alfresco in order to have a good understanding of how the applications are performing and to be aware of potential issues.
Features of Alfresco Search Services.
Features of Alfresco Search & Insight Engine.
Future plans for the product
---
DEMO GUIDE
[1] Queries: Share > Node Browser
ASPECT:'cm:titled' AND cm:title:'*Sample*' AND TEXT:'code'
SELECT * FROM cm:titled WHERE cm:title like '%Sample%' AND CONTAINS('code')
[2] Queries: Share > JS Console
var ctxt = Packages.org.springframework.web.context.ContextLoader.getCurrentWebApplicationContext();
var searchService = ctxt.getBean('SearchService', org.alfresco.service.cmr.search.SearchService);
var StoreRef = Packages.org.alfresco.service.cmr.repository.StoreRef;
var SearchService = Packages.org.alfresco.service.cmr.search.SearchService;
var ResultSet = Packages.org.alfresco.repo.search.impl.lucene.SolrJSONResultSet;
ResultSet =
searchService.query(
StoreRef.STORE_REF_WORKSPACE_SPACESSTORE,
SearchService.LANGUAGE_FTS_ALFRESCO,
"ASPECT:'cm:titled' AND cm:title:'*Sample*' AND TEXT:'code'");
logger.log(ResultSet.getNodeRefs());
---
var ctxt = Packages.org.springframework.web.context.ContextLoader.getCurrentWebApplicationContext();
var searchService = ctxt.getBean('SearchService', org.alfresco.service.cmr.search.SearchService);
var StoreRef = Packages.org.alfresco.service.cmr.repository.StoreRef;
var SearchService = Packages.org.alfresco.service.cmr.search.SearchService;
var ResultSet = Packages.org.alfresco.repo.search.impl.lucene.SolrJSONResultSet;
ResultSet =
searchService.query(
StoreRef.STORE_REF_WORKSPACE_SPACESSTORE,
SearchService.LANGUAGE_CMIS_ALFRESCO,
"SELECT * FROM cm:titled WHERE cm:title like '%Sample%' AND CONTAINS('code')");
logger.log(ResultSet.getNodeRefs());
---
var def =
{
query: "ASPECT:'cm:titled' AND cm:title:'*Sample*' AND TEXT:'code'",
language: "fts-alfresco"
};
var results = search.query(def);
logger.log(results);
[3] Queries: api-explorer
{
"query": {
"language": "afts",
"query": "ASPECT:\"cm:titled\" AND cm:title:\"*Sample\" AND TEXT:\"code\""
}
}
---
{
"query": {
"language": "cmis",
"query": "SELECT * FROM cm:titled WHERE cm:title like '%Sample%' AND CONTAINS('code')"
}
}
[4] Queries: CMIS Workbench > Groovy Console
rs = session.query("SELECT * FROM cm:titled WHERE cm:title like '%Sample%' AND CONTAINS('code')", false)
for (res in rs) {
println(res.getPropertyValueById('cmis:objectId'))
}
[5] Queries: SOLR Web Console > (alfresco) > Query
/afts
ASPECT:'cm:titled' AND cm:title:'*Sample*' AND TEXT:'code'
---
/cmis
SELECT * FROM cm:titled WHERE cm:title like '%Sample%' AND CONTAINS('code')
---
This is the session delivered during the Alfresco Developers Conference in Lisbon, January 2018. Learn all what you need to know to perform a proper backup and disaster recovery strategy. From a single server installation with hundreds of documents to a large deployment with multiple nodes, layers, databases and multi-million documents. What is the best way for each case?
The document discusses performance tuning of Alfresco. It covers JVM tuning including memory and garbage collection settings. It also discusses analyzing garbage collection logs and common problems. The document outlines different cache mechanisms in Alfresco including L1, L2 caches and Hazelcast caching. Tuning caches based on data change frequency and hit ratios is recommended. Finally, the document provides guidance on investigating performance issues by examining logs, threads, databases, storage and Alfresco/Solr configurations and settings.
Practical information for Alfresco integration with AOS (Sharepoint Protocol), Google Drive, Microsoft 365, ONLYOFFICE and Collabora Online.
Additionally ADW support for ONLYOFFICE is provided by https://github.com/atolcd/adf-onlyoffice-extension#installation
This document provides instructions for installing various Alfresco components, including:
1. PostgreSQL for the database
2. The Alfresco webapp using Tomcat
3. SOLR 6 for search
4. The Alfresco Share webapp also using Tomcat
It details downloading required software, configuring properties files, starting and stopping services, and ensuring the components can communicate over localhost URLs. The overall goal is to set up a full Alfresco ECM installation with database, application server, search, and user interface components locally for testing and development.
Alfresco has provided an implementation of CMIS ever since the first draft of the specification was announced. It is the CMIS repository that all others are compared to. In this session, you'll learn how Alfresco maps to the CMIS domain model and explore how CMIS services such as query behave through live examples. You'll see how easy it is to build applications against CMIS including the use of unique Alfresco features such as Aspects.
Infrastructure, use cases and performance considerations for
an Enterprise Grade ECM implementation up to 1B documents on AWS (Amazon Web Services EC2 and Aurora) based on the Alfresco (http://www.alfresco.com) Platform, leading Open Source Enterprise Content Management system.
This document discusses reindexing large repositories in Alfresco. It covers the Alfresco SOLR architecture, the indexing process, scenarios that require reindexing, alternatives for deployment during reindexing to minimize downtime, monitoring and profiling tools, and future improvements planned for Search Services 2.0 to optimize indexing performance. Benchmark results are presented showing improvements that reduced reindexing time for 1.2 billion documents from 21 days to 10 days.
Jose portillo dev con presentation 1138Jose Portillo
This document discusses best practices for implementing Solr sharding in Alfresco. It defines what sharding is and explains that it involves splitting a single index into multiple parts or shards to improve search performance, distribute indexing load, and scale horizontally. The document outlines different types of sharding, considerations for the number of shards, high availability, backup procedures, and common configuration settings when using Solr sharding in Alfresco.
The document provides an overview and best practices for tuning an Alfresco installation for performance. It discusses disabling unused services, limiting folder hierarchies and group nesting, monitoring resources, tuning Solr indexes and caches, and using separate servers for specific tasks like indexing. General tips include testing changes thoroughly before deploying, adjusting sizing for increased usage, and following the standard performance methodology.
This document discusses backup and disaster recovery strategies for Alfresco. It recommends scheduling regular backups of the Solr and Lucene indexes, database, and file system. Full backups should be done periodically, with incremental backups in between. Backups can be cold (system offline), warm (some services offline), or hot (live system). Restores involve recovering the indexes, database, files and configuration. Planning includes defining recovery objectives for data loss and downtime.
This document introduces the Alfresco Public API, which addresses limitations of Alfresco's existing API. The vision is for a single API that works across cloud and on-premise. The document explains how to get started with the API, including understanding OAuth2 authentication, registering an app, using a CMIS library, and making API calls. It provides an overview of entities and operations supported by the API.
The document discusses best practices for upgrading to Alfresco 6 from a previous version. It recommends backing up the database and content store from the source Alfresco, identifying any customizations, installing the new Alfresco from scratch, restoring the backups, applying customizations, and patching the database in stages if needed through intermediate "halfway" Alfresco instances. It also covers identifying deprecated features, adapting custom code to be compatible with Alfresco 6, monitoring the new installation, and addressing potential issues.
Support material for the blog post available in https://hub.alfresco.com/t5/alfresco-content-services-blog/alfresco-7-3-upgrading-to-transform-core-3-0-0/ba-p/315364
This presentation describes the differences between Alfresco Transform Engine and Alfresco Transform Core 3.0.0.
Deployment, configuration and extension topics for Transform Core are covered.
Making Structured Streaming Ready for ProductionDatabricks
In mid-2016, we introduced Structured Steaming, a new stream processing engine built on Spark SQL that revolutionized how developers can write stream processing application without having to reason about having to reason about streaming. It allows the user to express their streaming computations the same way you would express a batch computation on static data. The Spark SQL engine takes care of running it incrementally and continuously updating the final result as streaming data continues to arrive. It truly unifies batch, streaming and interactive processing in the same Datasets/DataFrames API and the same optimized Spark SQL processing engine.
The initial alpha release of Structured Streaming in Apache Spark 2.0 introduced the basic aggregation APIs and files as streaming source and sink. Since then, we have put in a lot of work to make it ready for production use. In this talk, Tathagata Das will cover in more detail about the major features we have added, the recipes for using them in production, and the exciting new features we have plans for in future releases. Some of these features are as follows:
- Design and use of the Kafka Source
- Support for watermarks and event-time processing
- Support for more operations and output modes
Speaker: Tathagata Das
This talk was originally presented at Spark Summit East 2017.
Unique course notes for the Certified Kubernetes Administrator (CKA) for each section of the exam. Designed to be engaging and used as a reference in the future for kubernetes concepts.
CUST-10 Customizing the Upload File(s) dialog in Alfresco ShareAlfresco Software
Many Alfresco projects require customizations to the Share user interface that go beyond the normal configuration. This usually involves changing/overriding Repository Web Scripts and Surf Web Scripts, updating JavaScript and CSS files, coding with the Yahoo UI Library, etc. This session will customize the Alfresco Share Upload File(s) dialog and show you how to: Add Widgets to the Upload File(s) dialog, Override Surf Web Scripts, Override/Update JavaScript and CSS files, Write Repository Web Scripts, Call Web Scripts from Yahoo UI Library code, and Setup a build project for these customizations. This session will present the advanced customization concepts via hands-on tutorial and slides.
This document provides an overview of Storage Foundation and Alfresco solutions. It discusses hardware storage concepts including drive types, interfaces, and RAID. It also covers Alfresco storage-related solutions such as the S3 connector, XAM connector, content store selector, and replication capabilities. Partnership solutions from Xenit, Star Storage, and community solutions are also mentioned. The document concludes with best practices around content store, indexes, logs, and backup/recovery.
This document summarizes a session from AWS re:Invent 2017 on migrating Microsoft applications to AWS. The session will provide an overview of why customers migrate to AWS, discuss general migration methodology, include deep dives into Active Directory, SQL Server, SharePoint, and Exchange migrations, and feature workshops and team presentations. It outlines the session timeline and topics to be covered.
(DAT309) Scaling Massive Content Stores with Amazon AuroraAmazon Web Services
John Newton, founder and CTO of Alfresco, describes how Amazon Aurora enables the Alfresco Content Management System to store, manage, and retrieve billions of documents and related information with fast and linear scalability. Using new techniques of information modeling, indexing, and processing with the recently launched Aurora database, Alfresco can support cloud-based workloads previously not possible for high-throughput insurance, banking, and case-based applications. This session addresses the challenges of scaling document repositories to this level; architectural approaches for coordinating data; search and storage technologies such as Aurora, Solr, Amazon EBS, and Amazon S3; the breadth of use cases that modern content systems need to support; and how to support user applications that require subsecond response times. The result is a solution that once would have required large data centers to support but can now be handled cost-effectively with AWS and Aurora.
Practical information for Alfresco integration with AOS (Sharepoint Protocol), Google Drive, Microsoft 365, ONLYOFFICE and Collabora Online.
Additionally ADW support for ONLYOFFICE is provided by https://github.com/atolcd/adf-onlyoffice-extension#installation
This document provides instructions for installing various Alfresco components, including:
1. PostgreSQL for the database
2. The Alfresco webapp using Tomcat
3. SOLR 6 for search
4. The Alfresco Share webapp also using Tomcat
It details downloading required software, configuring properties files, starting and stopping services, and ensuring the components can communicate over localhost URLs. The overall goal is to set up a full Alfresco ECM installation with database, application server, search, and user interface components locally for testing and development.
Alfresco has provided an implementation of CMIS ever since the first draft of the specification was announced. It is the CMIS repository that all others are compared to. In this session, you'll learn how Alfresco maps to the CMIS domain model and explore how CMIS services such as query behave through live examples. You'll see how easy it is to build applications against CMIS including the use of unique Alfresco features such as Aspects.
Infrastructure, use cases and performance considerations for
an Enterprise Grade ECM implementation up to 1B documents on AWS (Amazon Web Services EC2 and Aurora) based on the Alfresco (http://www.alfresco.com) Platform, leading Open Source Enterprise Content Management system.
This document discusses reindexing large repositories in Alfresco. It covers the Alfresco SOLR architecture, the indexing process, scenarios that require reindexing, alternatives for deployment during reindexing to minimize downtime, monitoring and profiling tools, and future improvements planned for Search Services 2.0 to optimize indexing performance. Benchmark results are presented showing improvements that reduced reindexing time for 1.2 billion documents from 21 days to 10 days.
Jose portillo dev con presentation 1138Jose Portillo
This document discusses best practices for implementing Solr sharding in Alfresco. It defines what sharding is and explains that it involves splitting a single index into multiple parts or shards to improve search performance, distribute indexing load, and scale horizontally. The document outlines different types of sharding, considerations for the number of shards, high availability, backup procedures, and common configuration settings when using Solr sharding in Alfresco.
The document provides an overview and best practices for tuning an Alfresco installation for performance. It discusses disabling unused services, limiting folder hierarchies and group nesting, monitoring resources, tuning Solr indexes and caches, and using separate servers for specific tasks like indexing. General tips include testing changes thoroughly before deploying, adjusting sizing for increased usage, and following the standard performance methodology.
This document discusses backup and disaster recovery strategies for Alfresco. It recommends scheduling regular backups of the Solr and Lucene indexes, database, and file system. Full backups should be done periodically, with incremental backups in between. Backups can be cold (system offline), warm (some services offline), or hot (live system). Restores involve recovering the indexes, database, files and configuration. Planning includes defining recovery objectives for data loss and downtime.
This document introduces the Alfresco Public API, which addresses limitations of Alfresco's existing API. The vision is for a single API that works across cloud and on-premise. The document explains how to get started with the API, including understanding OAuth2 authentication, registering an app, using a CMIS library, and making API calls. It provides an overview of entities and operations supported by the API.
The document discusses best practices for upgrading to Alfresco 6 from a previous version. It recommends backing up the database and content store from the source Alfresco, identifying any customizations, installing the new Alfresco from scratch, restoring the backups, applying customizations, and patching the database in stages if needed through intermediate "halfway" Alfresco instances. It also covers identifying deprecated features, adapting custom code to be compatible with Alfresco 6, monitoring the new installation, and addressing potential issues.
Support material for the blog post available in https://hub.alfresco.com/t5/alfresco-content-services-blog/alfresco-7-3-upgrading-to-transform-core-3-0-0/ba-p/315364
This presentation describes the differences between Alfresco Transform Engine and Alfresco Transform Core 3.0.0.
Deployment, configuration and extension topics for Transform Core are covered.
Making Structured Streaming Ready for ProductionDatabricks
In mid-2016, we introduced Structured Steaming, a new stream processing engine built on Spark SQL that revolutionized how developers can write stream processing application without having to reason about having to reason about streaming. It allows the user to express their streaming computations the same way you would express a batch computation on static data. The Spark SQL engine takes care of running it incrementally and continuously updating the final result as streaming data continues to arrive. It truly unifies batch, streaming and interactive processing in the same Datasets/DataFrames API and the same optimized Spark SQL processing engine.
The initial alpha release of Structured Streaming in Apache Spark 2.0 introduced the basic aggregation APIs and files as streaming source and sink. Since then, we have put in a lot of work to make it ready for production use. In this talk, Tathagata Das will cover in more detail about the major features we have added, the recipes for using them in production, and the exciting new features we have plans for in future releases. Some of these features are as follows:
- Design and use of the Kafka Source
- Support for watermarks and event-time processing
- Support for more operations and output modes
Speaker: Tathagata Das
This talk was originally presented at Spark Summit East 2017.
Unique course notes for the Certified Kubernetes Administrator (CKA) for each section of the exam. Designed to be engaging and used as a reference in the future for kubernetes concepts.
CUST-10 Customizing the Upload File(s) dialog in Alfresco ShareAlfresco Software
Many Alfresco projects require customizations to the Share user interface that go beyond the normal configuration. This usually involves changing/overriding Repository Web Scripts and Surf Web Scripts, updating JavaScript and CSS files, coding with the Yahoo UI Library, etc. This session will customize the Alfresco Share Upload File(s) dialog and show you how to: Add Widgets to the Upload File(s) dialog, Override Surf Web Scripts, Override/Update JavaScript and CSS files, Write Repository Web Scripts, Call Web Scripts from Yahoo UI Library code, and Setup a build project for these customizations. This session will present the advanced customization concepts via hands-on tutorial and slides.
This document provides an overview of Storage Foundation and Alfresco solutions. It discusses hardware storage concepts including drive types, interfaces, and RAID. It also covers Alfresco storage-related solutions such as the S3 connector, XAM connector, content store selector, and replication capabilities. Partnership solutions from Xenit, Star Storage, and community solutions are also mentioned. The document concludes with best practices around content store, indexes, logs, and backup/recovery.
This document summarizes a session from AWS re:Invent 2017 on migrating Microsoft applications to AWS. The session will provide an overview of why customers migrate to AWS, discuss general migration methodology, include deep dives into Active Directory, SQL Server, SharePoint, and Exchange migrations, and feature workshops and team presentations. It outlines the session timeline and topics to be covered.
(DAT309) Scaling Massive Content Stores with Amazon AuroraAmazon Web Services
John Newton, founder and CTO of Alfresco, describes how Amazon Aurora enables the Alfresco Content Management System to store, manage, and retrieve billions of documents and related information with fast and linear scalability. Using new techniques of information modeling, indexing, and processing with the recently launched Aurora database, Alfresco can support cloud-based workloads previously not possible for high-throughput insurance, banking, and case-based applications. This session addresses the challenges of scaling document repositories to this level; architectural approaches for coordinating data; search and storage technologies such as Aurora, Solr, Amazon EBS, and Amazon S3; the breadth of use cases that modern content systems need to support; and how to support user applications that require subsecond response times. The result is a solution that once would have required large data centers to support but can now be handled cost-effectively with AWS and Aurora.
The document discusses Netflix's use of Elasticsearch for querying log events. It describes how Netflix evolved from storing logs in files to using Elasticsearch to enable interactive exploration of billions of log events. It also summarizes some of Netflix's best practices for running Elasticsearch at scale, such as automatic sharding and replication, flexible schemas, and extensive monitoring.
n this session, we'll simplify the complexities of configuring and troubleshooting mutual TLS (mTLS) within Alfresco environments. Attendees will gain practical insights into certificate management, trust validation, and common challenges encountered during configuration.
We'll showcase and provide custom tools for troubleshooting during the session. These tools can be used with ZIP, Ansible, Docker and Kubernetes deployments.
Event description available in https://hub.alfresco.com/t5/news-announcements/ttl-157-troubleshooting-made-easy-deciphering-alfresco-s-mtls/ba-p/319735/jump-to/first-unread-message
How leading financial services organisations are winning with techMongoDB
Financial services organizations are adopting new technologies like cloud computing, big data, artificial intelligence, blockchain, and Internet of Things to improve business agility, reduce costs, and gain new insights from data. MongoDB is helping in areas like cloud data strategy, blockchain applications, mainframe offloading, and powering Internet of Things applications by providing a flexible, scalable database that can be deployed across on-premises, private cloud, and public cloud environments.
SQL is a powerful tool to query data, but it doesn't cover everything you might need. Sometimes, the precision of SQL is a limitation, that can be overcome by using the flexibility and inherent ranking of search. Learn how to use AWS servcies to create fully managed solutions using Amazon Aurora and Amazon Elasticsearch Service to combine the power of query and search.
Elasticsearch is recommended to create an archive to search ACM/BPM case and process data that is up to 7 years old. Elasticsearch allows storing and searching large volumes of data quickly and in near real-time. It was tested by uploading over 40,000 documents from a use case involving tweets. This allowed full-text search of case data and searching within office documents. While Elasticsearch is schema-less and easy to evolve with Oracle releases, its limitations regarding transactions and an overview of case history would need to be considered.
by Darin Briskman, Technical Evangelist, AWS
SQL is a powerful tool to query data, but it doesn't cover everything you might need. Sometimes, the precision of SQL is a limitation, that can be overcome by using the flexibility and inherent ranking of search. Learn how to use AWS servcies to create fully managed solutions using Amazon Aurora and Amazon Elasticsearch Service to combine the power of query and search. Level: 200
The document discusses various topics related to developing successful cloud services, including microservices architecture, Platform as a Service (PaaS), multi-tenancy, and DevOps. It defines a successful service as one that offers subscription-based delivery according to an international survey. Characteristics of successful services include automation, monetization through subscriptions, implementation using techniques like mashups and multi-tenancy, and using microservices focusing on separation of concerns. The document outlines a journey to developing successful services and discusses related topics like operational automation, revenue generation, implementation approaches, and microservices.
Scaling the Content Repository with ElasticsearchNuxeo
This talk will explain how to leverage Elasticsearch capabilities to make your content repository scale to the sky while still relying on standard SQL based technologies and ensuring data security and integrity. The design choices behind this hybrid Elasticsearch / PgSQL architecture will be discussed and the technical integration with Elasticsearch will be demonstrated.
Watch the recorded webinar: http://www.nuxeo.com/resources/scaling-the-document-repository-with-elasticsearch/
MicroStrategy on Amazon Web Services (AWS) CloudCCG
The document discusses MicroStrategy cloud offerings on Amazon Web Services (AWS). It describes MicroStrategy as providing a comprehensive enterprise analytics platform in the cloud, including business intelligence, analytical databases, and data integration. The platform is optimized for performance and scalability using best-of-breed technologies. Customers benefit from experts managing and monitoring the analytics platform.
Join this workshop to understand the core concepts of “Cloud Computing” and how businesses around the world are running the infrastructure that supports their websites to lower costs, improve time-to-market, and enable rapid scalability matching resource to demands of users. Whether you are an enterprise looking for IT innovation, agility and resiliency or small and medium business who wants to accelerate growth without a big upfront investment in cash or time for technology, the AWS Cloud provides a complete set of services at zero upfront costs which are available with a few clicks and within minutes.
Webinar: Unlock the Power of Streaming Data with Kinetica and ConfluentKinetica
The volume, complexity and unpredictability of streaming data is greater than ever before. Innovative organizations require instant insight from streaming data in order to make real-time business decisions. A new technology stack is emerging as traditional databases and data lakes are challenged to analyze streaming data and historical data together in real time.
Confluent Platform, a more complete distribution of Apache Kafka®, works with Kinetica’s GPU-accelerated engine to transform data on the wire, instantly ingest data and analyze it at the same time. With the Kinetica Connector, end users can ingest streaming data from sensors, mobile apps, IoT devices and social media via Kafka into Kinetica’s database to combine it with data at rest. Together, the technologies deliver event-driven and real-time data to power the speed of thought analytics, improve customer experience, deliver targeted marketing offers and increase operational efficiencies.
BDA402 Deep Dive: Log Analytics with Amazon Elasticsearch ServiceAmazon Web Services
Everything generates logs. Applications, infrastructure, security ... everything. Keeping track of the flood of log data is a big challenge, yet critical to your ability to understand your systems and troubleshoot (or prevent) issues. In this session, we will use both Amazon CloudWatch and application logs to show you how to build an end-to-end log analytics solution. First, we cover how to configure an Amazon Elaticsearch Service domain and ingest data into it using Amazon Kinesis Firehose, demonstrating how easy it is to transform data with Firehose. We look at best practices for choosing instance types, storage options, shard counts, and index rotations based on the throughput of incoming data and configure a secure analytics environment. We demonstrate how to set up a Kibana dashboard and build custom dashboard widgets. Finally, we dive deep into the Elasticsearch query DSL and review approaches for generating custom, ad-hoc reports.
How to Architect a Serverless Cloud Data Lake for Enhanced Data AnalyticsInformatica
This presentation is geared toward enterprise architects and senior IT leaders looking to drive more value from their data by learning about cloud data lake management.
As businesses focus on leveraging big data to drive digital transformation, technology leaders are struggling to keep pace with the high volume of data coming in at high speed and rapidly evolving technologies. What's needed is an approach that helps you turn petabytes into profit.
Cloud data lakes and cloud data warehouses have emerged as a popular architectural pattern to support next-generation analytics. Informatica's comprehensive AI-driven cloud data lake management solution natively ingests, streams, integrates, cleanses, governs, protects and processes big data workloads in multi-cloud environments.
Please leave any questions or comments below.
IBM Omnifind Enterprise Portal Seach To Improve ProductivityFrancis Ricalde
The document discusses IBM's OmniFind Enterprise Edition product which improves productivity by allowing knowledge workers to more easily find the right information at the right time in the proper context. It provides enterprise-wide search capabilities that go beyond basic keyword search and integrates with WebSphere Portal. Key features include searching both inside and outside portal repositories, advanced search features, multiple language support, and high availability.
Accelerating analytics in the cloud with the Starburst Presto + Alluxio stackAlluxio, Inc.
Alluxio Tech Talk
January 21, 2020
Speakers:
Matt Fuller, Starburst
Dipti Borkar, Alluxio
With the advent of the public clouds and data increasingly siloed across many locations -- on premises and in the public cloud -- enterprises are looking for more flexibility and higher performance approaches to analyze their structured data.
Join us for this tech talk where we’ll introduce the Starburst Presto, Alluxio, and cloud object store stack for building a highly-concurrent and low-latency analytics platform. This stack provides a strong solution to run fast SQL across multiple storage systems including HDFS, S3, and others in public cloud, hybrid cloud, and multi-cloud environments. You’ll learn more about:
- The architecture of Presto, an open source distributed SQL engine
- How the Presto + Alluxio stack queries data from cloud object storage like S3 for faster and more cost-effective analytics
- Achieving data locality and cross-job caching with Alluxio regardless of where data is persisted
Ben King, a Solutions Engineer from Atlassian, and Shiva N, an AWS Solution Architect, gave a presentation on deploying Atlassian's Data Center products on AWS. They discussed how using AWS provides flexibility, resiliency and ability to scale. The presentation covered preparing, deploying and refining a Data Center implementation on AWS, including using services like EC2, ELB, RDS, EFS, CloudFormation and CloudWatch. The goal was to show how running Data Center on AWS allows organizations to focus on their core mission rather than infrastructure.
Activating your Data, with a Faster Path to Results - Aaron Murphy, CommvaultLucidworks
Commvault is a data management software company that manages large amounts of customer data across various platforms and locations. The document discusses Commvault's Activate product, which aims to provide faster access to data through search and analytics capabilities. Activate allows users to search indexed data, access copies of data on-demand for various uses like testing and development, and derive insights through features like clustering and natural language processing. It provides a single platform to manage data across its lifecycle and help users make more informed decisions.
Alluxio Data Orchestration Platform for the CloudShubham Tagra
Alluxio originated as an open source project at UC Berkeley to orchestrate data for cloud applications by providing a unified namespace and intelligent data caching across multiple data sources. It provides consistent high performance for analytics and AI workloads running on object stores by caching frequently accessed data in memory and tiering data to flash/disk based on policies. Alluxio can also enable hybrid cloud environments by allowing on-premises workloads to burst to public clouds without data movement through "zero-copy" access to remote data.
Similaire à How to migrate from Alfresco Search Services to Alfresco SearchEnterprise (20)
Using Generative AI and Content Service Platforms togetherAngel Borroy López
Slides for FOSDEM 2024 session: https://fosdem.org/2024/schedule/event/fosdem-2024-1858-using-generative-ai-and-content-service-platforms-together/
Describes a framework that provides GenAI operations for documents using a REST API. LLMs are stored locally, so no data is sent away.
It also includes a sample integration with a Content Service Platform (Alfresco), to enhance documents and pictures context information.
Session recording is available in https://ftp.fau.de/fosdem/2024/h2213/fosdem-2024-1858-using-generative-ai-and-content-service-platforms-together.av1.webm
Enhancing Document-Centric Features with On-Premise Generative AI for Alfresc...Angel Borroy López
Oractical guide on integrating Alfresco Community with On-Premise Generative AI.
This session outlines the steps to enhance both existing and new content, demonstrating features such as classification, summarization, translation, and prompting. But this framework allows you to include additional features.
Source code is available in https://github.com/aborroy/alfresco-genai
This presentation describes different methods to produce Alfresco Docker Assets for Docker Compose deployment.
From the previous methods (based in Python, Yeoman and Docker) to the Docker Init with Templates approach.
The recent launch of the Docker Init command has significantly simplified the process of generating Dockerfiles and Docker Compose templates for containerized applications. This presentation aims to explore the evolution of Docker deployment resources generation process, comparing its approach prior to the Docker Init command release and discussing the way forward. Before the introduction of the Docker Init command, I've been delivering some projects like the "alfresco-docker-installer"[1], which provides custom scripts and configurations to streamline the process of deploying Alfresco in Docker containers. These kinds of projects use tools like Yeoman or raw Python. There are some differences between a Docker Template for a technology (Go, Python, Node or Rust) and a Docker Template for a product (like Alfresco) that may be covered when generating automatic deployment resources. This presentation will delve into the methodologies employed before the Docker Init command:
Custom Dockerfile Extension
Compose Template for a complete product deployment, including a set of services like the database, content repository, search engine, or web application
Configuration Management, including techniques such as environment variable injection, externalized configuration files, and configuration overrides
Following the release of the Docker Init command, this presentation will provide insights into the possibilities and advantages it brings to complex products Docker deployment process. A PoC of a Docker Plugin, including this product-oriented approach for docker init, will be demoed live. >> Note that the Open Source Alfresco product is used only to explain the concepts of building a Docker Compose generator with a real example.
This deck includes a description of the Transform Service available for Alfresco 7.4.0.
Secure configuration sample, relying on mTLS, is also discussed.
This presentation describes how to use Podman to replace Docker in the Alfresco 7.4.0 development process.
Alfresco platform is built using containerization technology. Alfresco can utilize containerization platforms like Podman, which provide the necessary tools and infrastructure to create, manage, and run containers.
Podman is presented as an alternative to Docker. Both Docker and Podman can be used effectively for Alfresco development. So consider your familiarity with the tools, preferred workflow, ecosystem support, security requirements, and any specific performance considerations to make the best choice for your Alfresco development needs.
CSP: Evolución de servicios de código abierto en un mundo Cloud NativeAngel Borroy López
Presentación realizada en Openexpo Europe 2023:
https://openexpoeurope.com/es/session/cuando-hyland-encontro-a-alfresco-evolucion-de-servicios-de-codigo-abierto-en-un-mundo-cloud-native/
Presenta una visión evolutiva de las plataformas de gestión documental: ECM, CSP y Cloud Native.
Incluye información relevante de los productos Alfresco, Nuxeo y Hyland Experience.
This presentation describes how to use the BPM Engine included with Alfresco ACS repository.
All the different APIs are covered: Workflow Console UI, REST API and Java API.
Este documento proporciona recursos para aprender Docker, incluyendo documentación, libros, videos de YouTube y la comunidad Docker. Explica cómo instalar Docker en Windows, Mac y Linux, y cubre herramientas como Docker Desktop y Docker Hub. También describe los planes de suscripción disponibles para Docker.
Docker 101 - Zaragoza Docker Meetup - Universidad de ZaragozaAngel Borroy López
This document provides an introduction to Docker presented at a Docker Zaragoza Meetup. It discusses Docker Engine, images and containers, Docker architecture, creating images with Dockerfiles, sharing images with Docker registries like Docker Hub, and hands-on exercises using Docker Classroom and Play with Docker. The presentation introduces key Docker concepts and components to help attendees discover Docker and get started using it.
The document discusses how to write Alfresco addons that last a long time. It covers different technologies used in Alfresco over time, including the Alfresco Developer Framework (ADF), workflows, integrations, extension points, and best practices. The key to writing enduring addons is to use extension points and avoid private/deprecated APIs, and to transition technologies like actions and workflows to newer approaches like microservices and the Alfresco Content Application.
The document provides 10 tips for new developers working with Alfresco. It discusses the key technologies used in Alfresco including Java versions, Spring Framework, Angular, SOLR, BPM, REST APIs, Docker, and Kubernetes. For each tip it highlights the relevant technologies and concepts a new developer should understand when working with Alfresco.
The document discusses deploying Activiti Cloud, an open source process automation platform, using Kubernetes, Helm, and Docker. It provides an overview of the software components including Activiti 7 for process automation, Keycloak for identity management, and how Helm charts can be used to package and deploy Docker images, services, and infrastructure configurations to Kubernetes. Steps are outlined for deploying an example Activiti Cloud implementation on a Kubernetes cluster using Helm.
The Comprehensive Guide to Validating Audio-Visual Performances.pdfkalichargn70th171
Ensuring the optimal performance of your audio-visual (AV) equipment is crucial for delivering exceptional experiences. AV performance validation is a critical process that verifies the quality and functionality of your AV setup. Whether you're a content creator, a business conducting webinars, or a homeowner creating a home theater, validating your AV performance is essential.
WWDC 2024 Keynote Review: For CocoaCoders AustinPatrick Weigel
Overview of WWDC 2024 Keynote Address.
Covers: Apple Intelligence, iOS18, macOS Sequoia, iPadOS, watchOS, visionOS, and Apple TV+.
Understandable dialogue on Apple TV+
On-device app controlling AI.
Access to ChatGPT with a guest appearance by Chief Data Thief Sam Altman!
App Locking! iPhone Mirroring! And a Calculator!!
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
React.js, a JavaScript library developed by Facebook, has gained immense popularity for building user interfaces, especially for single-page applications. Over the years, React has evolved and expanded its capabilities, becoming a preferred choice for mobile app development. This article will explore why React.js is an excellent choice for the Best Mobile App development company in Noida.
Visit Us For Information: https://www.linkedin.com/pulse/what-makes-reactjs-stand-out-mobile-app-development-rajesh-rai-pihvf/
Odoo releases a new update every year. The latest version, Odoo 17, came out in October 2023. It brought many improvements to the user interface and user experience, along with new features in modules like accounting, marketing, manufacturing, websites, and more.
The Odoo 17 update has been a hot topic among startups, mid-sized businesses, large enterprises, and Odoo developers aiming to grow their businesses. Since it is now already the first quarter of 2024, you must have a clear idea of what Odoo 17 entails and what it can offer your business if you are still not aware of it.
This blog covers the features and functionalities. Explore the entire blog and get in touch with expert Odoo ERP consultants to leverage Odoo 17 and its features for your business too.
An Overview of Odoo ERP
Odoo ERP was first released as OpenERP software in February 2005. It is a suite of business applications used for ERP, CRM, eCommerce, websites, and project management. Ten years ago, the Odoo Enterprise edition was launched to help fund the Odoo Community version.
When you compare Odoo Community and Enterprise, the Enterprise edition offers exclusive features like mobile app access, Odoo Studio customisation, Odoo hosting, and unlimited functional support.
Today, Odoo is a well-known name used by companies of all sizes across various industries, including manufacturing, retail, accounting, marketing, healthcare, IT consulting, and R&D.
The latest version, Odoo 17, has been available since October 2023. Key highlights of this update include:
Enhanced user experience with improvements to the command bar, faster backend page loading, and multiple dashboard views.
Instant report generation, credit limit alerts for sales and invoices, separate OCR settings for invoice creation, and an auto-complete feature for forms in the accounting module.
Improved image handling and global attribute changes for mailing lists in email marketing.
A default auto-signature option and a refuse-to-sign option in HR modules.
Options to divide and merge manufacturing orders, track the status of manufacturing orders, and more in the MRP module.
Dark mode in Odoo 17.
Now that the Odoo 17 announcement is official, let’s look at what’s new in Odoo 17!
What is Odoo ERP 17?
Odoo 17 is the latest version of one of the world’s leading open-source enterprise ERPs. This version has come up with significant improvements explained here in this blog. Also, this new version aims to introduce features that enhance time-saving, efficiency, and productivity for users across various organisations.
Odoo 17, released at the Odoo Experience 2023, brought notable improvements to the user interface and added new functionalities with enhancements in performance, accessibility, data analysis, and management, further expanding its reach in the market.
WMF 2024 - Unlocking the Future of Data Powering Next-Gen AI with Vector Data...Luigi Fugaro
Vector databases are transforming how we handle data, allowing us to search through text, images, and audio by converting them into vectors. Today, we'll dive into the basics of this exciting technology and discuss its potential to revolutionize our next-generation AI applications. We'll examine typical uses for these databases and the essential tools
developers need. Plus, we'll zoom in on the advanced capabilities of vector search and semantic caching in Java, showcasing these through a live demo with Redis libraries. Get ready to see how these powerful tools can change the game!
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
🏎️Tech Transformation: DevOps Insights from the Experts 👩💻campbellclarkson
Connect with fellow Trailblazers, learn from industry experts Glenda Thomson (Salesforce, Principal Technical Architect) and Will Dinn (Judo Bank, Salesforce Development Lead), and discover how to harness DevOps tools with Salesforce.
The Power of Visual Regression Testing_ Why It Is Critical for Enterprise App...kalichargn70th171
Visual testing plays a vital role in ensuring that software products meet the aesthetic requirements specified by clients in functional and non-functional specifications. In today's highly competitive digital landscape, users expect a seamless and visually appealing online experience. Visual testing, also known as automated UI testing or visual regression testing, verifies the accuracy of the visual elements that users interact with.
Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...XfilesPro
Wondering how X-Sign gained popularity in a quick time span? This eSign functionality of XfilesPro DocuPrime has many advancements to offer for Salesforce users. Explore them now!
DevOps Consulting Company | Hire DevOps Servicesseospiralmantra
Spiral Mantra excels in providing comprehensive DevOps services, including Azure and AWS DevOps solutions. As a top DevOps consulting company, we offer controlled services, cloud DevOps, and expert consulting nationwide, including Houston and New York. Our skilled DevOps engineers ensure seamless integration and optimized operations for your business. Choose Spiral Mantra for superior DevOps services.
https://www.spiralmantra.com/devops/
Orca: Nocode Graphical Editor for Container OrchestrationPedro J. Molina
Tool demo on CEDI/SISTEDES/JISBD2024 at A Coruña, Spain. 2024.06.18
"Orca: Nocode Graphical Editor for Container Orchestration"
by Pedro J. Molina PhD. from Metadev
Baha Majid WCA4Z IBM Z Customer Council Boston June 2024.pdfBaha Majid
IBM watsonx Code Assistant for Z, our latest Generative AI-assisted mainframe application modernization solution. Mainframe (IBM Z) application modernization is a topic that every mainframe client is addressing to various degrees today, driven largely from digital transformation. With generative AI comes the opportunity to reimagine the mainframe application modernization experience. Infusing generative AI will enable speed and trust, help de-risk, and lower total costs associated with heavy-lifting application modernization initiatives. This document provides an overview of the IBM watsonx Code Assistant for Z which uses the power of generative AI to make it easier for developers to selectively modernize COBOL business services while maintaining mainframe qualities of service.
Using Query Store in Azure PostgreSQL to Understand Query PerformanceGrant Fritchey
Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.
Superpower Your Apache Kafka Applications Development with Complementary Open...Paul Brebner
Kafka Summit talk (Bangalore, India, May 2, 2024, https://events.bizzabo.com/573863/agenda/session/1300469 )
Many Apache Kafka use cases take advantage of Kafka’s ability to integrate multiple heterogeneous systems for stream processing and real-time machine learning scenarios. But Kafka also exists in a rich ecosystem of related but complementary stream processing technologies and tools, particularly from the open-source community. In this talk, we’ll take you on a tour of a selection of complementary tools that can make Kafka even more powerful. We’ll focus on tools for stream processing and querying, streaming machine learning, stream visibility and observation, stream meta-data, stream visualisation, stream development including testing and the use of Generative AI and LLMs, and stream performance and scalability. By the end you will have a good idea of the types of Kafka “superhero” tools that exist, which are my favourites (and what superpowers they have), and how they combine to save your Kafka applications development universe from swamploads of data stagnation monsters!
Transforming Product Development using OnePlan To Boost Efficiency and Innova...OnePlan Solutions
Ready to overcome challenges and drive innovation in your organization? Join us in our upcoming webinar where we discuss how to combat resource limitations, scope creep, and the difficulties of aligning your projects with strategic goals. Discover how OnePlan can revolutionize your product development processes, helping your team to innovate faster, manage resources more effectively, and deliver exceptional results.
13. SCALING UP : INDEXING
content NoC < 10
Increasing the number of consumers doesn’t help
Number of Pending Messages +++
metadata NoC++
Increase the number of metadata consumers
Search Engine Resources++
Increase RAM / CPU of Cluster
Alternatively, add a new node to the Cluser
(shards are managed internally)
mediation NoC++
Increase the number of mediation consumers
14. SCALING UP : SEARCHING
https://www.elastic.co/guide/en/elasticsearch/reference/current/tune-for-search-speed.html
https://opensearch.org/docs/latest/search-plugins/knn/performance-tuning/#search-performance-tuning
§ Configure HEAP with ES_HEAP_SIZE
• Avoid using directly -Xms -Xmx
• Leave ½ of the memory available to Lucene
§ Reduce swapping
• Using bootstrap.mlockall:true property prevents JVM to swap
§ Set the right buffer size for indexing with
indices.memory.index_buffer_size
• Each Shard requires around 512 mb but increasing it doesn’t help
15. SCALING UP : REINDEXING WITH REMOTE PARTITIONS
alfresco-elasticsearch-reindexing
alfresco-elasticsearch-reindexing
alfresco-elasticsearch-reindexing
alfresco-activemq
MANAGER
DB Schema Validation
Partition creation
Reindexing
Database
WORKER 1
Read partition
Index nodes
WORKER N
Read partition
Index nodes
Alfresco
Database
Produce partition requests >
< Consume workers replies
…
< partition
< partition
reply >
reply >
Partition
By ID Range
By Date Range
18. WHY TO ADOPT SEARCH ENTERPRISE
§ Supporting Elasticsearch and OpenSearch out-of-the-box products
§ Documents and values are available for searching sooner
§ Re-indexing large repositories is much faster, moving the scale of
time from days to hours
§ Scaling up the search engine is easier, and it can be almost
transparent when using managed services
§ Cloud deployment based in Kubernetes is better supported
§ Alfresco Search products based in SOLR are currently in
maintenance mode
19. DESIGNING YOUR PATH TO SEARCH ENTERPRISE
Upgraded
ACS
Compatibility
Existing
ACS
ACS >= 7.1
Upgrade
ACS
Rewrite
queries
Downtime
Parallel
Indexing
Unsupported Features Supported Platforms Zero Downtime Upgrade
20. COMPATIBILITY VERIFICATION
Review Unsupported Features documentation
§ Field Queries like DBID, TXID, ACLID, ANCESTOR or FTSSTATUS
§ SQL query language using JDBC Driver
§ Data types like any, assocref, locale and qname
Audit your ACS Deployment
§ Build a query catalog for your use case
§ Approach using the Audit Subsystem is available in
• https://github.com/AlfrescoLabs/alfresco-query-catalog-builder
Run a dry run on a testing environment using Search Enterprise with
your data and your apps