Hüseyin Akdoğan

RFC 10008's new QUERY method is safe and cacheable like GET but carries content like POST. This article explains the spec and runs it on Quarkus today.

Bearer tokens grant access to anyone who holds them. DPoP fixes this by binding each token to a cryptographic key pair: every request must include a signed proof.

Gatherers is a new and powerful API that enhances the Stream API by modeling intermediate operations and allowing the definition of custom intermediate operations.

Hibernate Search is a library that allows keeping your local Lucene indexes or ElasticSearch cluster in sync with your database

We quickly go over how to use Java and Vert.x to limit the amount of traffic handled by our application.

For the eaiser crafting of Docker images from source code.

Want to learn more about how to share data between threads in Vert.x? Click here to learn more about multi-threading in Java.

Want to learn how to run blocking code in Vert.x? Check out this post to learn more about blocking code and synchronous APIs.

Learn how to work with multiples verticles in Vert.x clusters between multiple applications in a clustered environment.

Want to learn more about working with multiple verticles and point-to-point messaging in Vert.x? Check out this tutorial to learn more!

Want to learn more about Eclipse Vert.x? Check out this post on the Eclipse Vert.x toolkit for developing reactive applications on JVM.

Learn how to implement container technologies with your Jenkins CI/CD workflows to make them easier to manage in this tutorial.

Compared to other features, real-time search capability is undoubtedly one of the most important features in Elasticsearch. Today we’ll look closely how is provided real-time search by Elasticsearch. Real time First of all, if we need to explain the concept of real-time, in general, we can say that the delay between input and out time in the information is small at real-time systems. This means, data is taken without data accumulation, processed in real time. Today, the best solution Elasticsearch known for real-time search, when a record is added to it for storage makes it searchable in 1 second. How? As is known, the disks are able to create a risk of bottleneck for I/O operations at the data persistence step. Also some mechanisms used for prevent any loss of data increases cost of time. At this point Elasticsearch uses the file-system cache that sitting between itself and the disk for overcome the risk of bottleneck and ensure the a new document can be searched in real time. A new segment is written to the file-system cache first and only later it flushed to disk by Elasticsearch. This lightweight process of writing and opening a new segment is called a refresh in Elasticsearch. By default, all shards is refreshed automatically once every second. In this way, Elasticsearch support real-time search. Test time Above digression about the time of refresh of the shards you can bring to mind the following questions: What happens, when a new document is requested in less than 1 second time? Can be documents requested, without having to depend of the refresh period shards of managed by Elasticsearch? Short answers. Elasticsearch does not return the document. Yes. Now let’s get clarity on this issue is a simple example. hakdogan$ curl -XPUT localhost:9200/kodcucom/document/1 -d'{ > "title": "Document A" > }' We sent a document to Elasticsearch. The index name is kodcucom, type document, id value 1. The title field is only field in the document and the value of "Document A". Let’s take this document from Elasticsearch. hakdogan$ curl -XGET localhost:9200/kodcucom/document/1?pretty { "_index" : "kodcucom", "_type" : "document", "_id" : "1", "_version" : 1, "found" : true, "_source":{ "title": "Document A" } } As expected, the document was returned to us. Well, if we keep short the time between document recording and get request than default shard refresh time what will happen? Let’s see. hakdogan$ curl -XPUT localhost:9200/kodcucom/document/2 -d'{"title": "Document B"}'; curl -XGET localhost:9200/kodcucom/_search?pretty {"_index":"kodcucom","_type":"document","_id":"2","_version":1,"created":true}{ "took" : 38, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "failed" : 0 }, "hits" : { "total" : 1, "max_score" : 1.0, "hits" : [ { "_index" : "kodcucom", "_type" : "document", "_id" : "1", "_score" : 1.0, "_source":{ "title": "Document A" } } ] } } As can be seen, only the previous document was returned to us by Elasticsearch when we do concurrently create and get request. Well, how can I get the document concurrently? Let’s see. hakdogan$ curl -XPUT localhost:9200/kodcucom/document/3 -d'{"title": "Document C"}'; curl -XGET localhost:9200/kodcucom/_refresh; curl -XGET localhost:9200/kodcucom/_search?pretty {"_index":"kodcucom","_type":"document","_id":"3","_version":1,"created":true}{"_shards":{"total":10,"successful":5,"failed":0}{ "took" : 3, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "failed" : 0 }, "hits" : { "total" : 3, "max_score" : 1.0, "hits" : [ { "_index" : "kodcucom", "_type" : "document", "_id" : "1", "_score" : 1.0, "_source":{ "title": "Document A" } }, { "_index" : "kodcucom", "_type" : "document", "_id" : "2", "_score" : 1.0, "_source":{"title": "Document B"} }, { "_index" : "kodcucom", "_type" : "document", "_id" : "3", "_score" : 1.0, "_source":{"title": "Document C"} } ] } } In this command, we perform to refresh operation on kodcucom index before the search request. In this way, the document was returned to us. Auto refresh time can be changed. By setting the index.refresh_interval parameter in the configuration file. Applies to all indices in the cluster. A per-index basis by updated index setting. In addition to these, you can turn off automatic refresh. An important point to keep in mind about the refresh time of the shards, the refresh operation is costly in terms of system resources. If you wished to make changes to the auto-refresh time, this situation should be taken into account. Extension of the automatic refresh time, enables faster indexing but new documents and changes made to the existing documents will not appear in searches during specified period of time.

Apache Shiro can be used in a wide range of applications as part of the Java Security Framework.

ElasticSearch provides Java API, thus it executes all operations asynchronously by using client object.

Learn about ElasticSearch, an open source tool developed with Java. It is a Lucene-based, scalable, full-text search engine, and a data analysis tool.

EclipseLink, reference implementation of JPA, has JPA support for NoSQL databases (MongoDB and Oracle NoSQL) as of the version 2.4. In this tutorial we will discuss the use of MongoDB database with the JPA support of EclipseLink. The transaction previously done using the console and native java driver will be done in a web application with the help of EclipseLink. Tools and technologies used in the sample application are as follows: MongoDB version 2.4.1 MongoDB Java Driver version 2.11.1 JSF version 2.2 PrimeFaces version 3.5 EclipseLink version 2.4 Jetty 7.x Maven Plugin JDK version 1.7 Maven 3.0.4 Project Dependencies org.glassfish javax.faces 2.2.0-SNAPSHOT org.primefaces primefaces 3.5 org.primefaces.themes bootstrap 1.0.10 org.eclipse.persistence org.eclipse.persistence.jpa 2.4.0-SNAPSHOT org.eclipse.persistence org.eclipse.persistence.nosql 2.4.0-SNAPSHOT jboss jboss-j2ee 4.2.2.GA org.mongodb mongo-java-driver 2.11.1 commons-fileupload commons-fileupload 1.3 Entity Class @Entity @NoSql(dataFormat=DataFormatType.MAPPED) public class Article implements Serializable { public Article() { } @Id @GeneratedValue @Field(name="_id") private String id; @ElementCollection private List categoryLists = new ArrayList(); @Basic private String title; @Basic private String content; @Basic @Temporal(javax.persistence.TemporalType.DATE) private Date date; @Basic private String author; @ElementCollection private List tagLists = new ArrayList(); @NoSQL notation sets the data format and type and maps the NoSQL data. Because of using MongoDB in our sample application and documents in MongoDB stored in BSON format, MAP is used as data type. @ElementCollection notation maps the embedded collection into the parent document. Because more than one category and tag associated with an article would be a matter in our sample application, we map them as an element collection. Embedded Objects @Embeddable @NoSql(dataFormat=DataFormatType.MAPPED) public class Categories implements Serializable { @Basic private String category; @Embeddable @NoSql(dataFormat=DataFormatType.MAPPED) public class Tags implements Serializable { @Basic private String tag; We see @Embeddable notation at the top of the Categories and Tags’ class unlike Article entity class. The documents stored in the parent document are mapped with this notation. Please note that embedded objects do not need unique field. persistence.xml com.kodcu.entity.Article com.com.kodcu.entity.Categories com.kodcu.entity.Tags CRUD Operations index.xhtml MyBean.java public void saveArticle() { em.getTransaction().begin(); if(null == article.getId()) em.persist(article); else em.merge(article); em.getTransaction().commit(); } public void removeArticle() { em.getTransaction().begin(); em.remove(selectArticle); em.getTransaction().commit(); } 6. Demo Application Real content above and the demo application, can be accessed at NoSQL with JPA