Apache Solr Tutorial PDF Version Quick Guide Resources Job Search Discussion Solr is a scalable, ready to deploy, search/storage engine optimized to search large volumes of text-centric data. Solr is enterprise-ready, fast and highly scalable. In this tutorial, we are going to learn the basics of Solr and how you can use it in practice. Audience This tutorial will be helpful for all those developers who would like to understand the basic functionalities of Apache Solr in order to develop sophisticated and high-performing applications. Prerequisites Before proceeding with this tutorial, we expect that the reader has good Java programming skills (although it is not mandatory) and some prior exposure to Lucene and Hadoop environment. Print Page Previous Next Advertisements ”;
Category: apache Solr
Apache Solr – Overview
Apache Solr – Overview ”; Previous Next Solr is an open-source search platform which is used to build search applications. It was built on top of Lucene (full text search engine). Solr is enterprise-ready, fast and highly scalable. The applications built using Solr are sophisticated and deliver high performance. It was Yonik Seely who created Solr in 2004 in order to add search capabilities to the company website of CNET Networks. In Jan 2006, it was made an open-source project under Apache Software Foundation. Its latest version, Solr 6.0, was released in 2016 with support for execution of parallel SQL queries. Solr can be used along with Hadoop. As Hadoop handles a large amount of data, Solr helps us in finding the required information from such a large source. Not only search, Solr can also be used for storage purpose. Like other NoSQL databases, it is a non-relational data storage and processing technology. In short, Solr is a scalable, ready to deploy, search/storage engine optimized to search large volumes of text-centric data. Features of Apache Solr Solr is a wrap around Lucene’s Java API. Therefore, using Solr, you can leverage all the features of Lucene. Let us take a look at some of most prominent features of Solr − Restful APIs − To communicate with Solr, it is not mandatory to have Java programming skills. Instead you can use restful services to communicate with it. We enter documents in Solr in file formats like XML, JSON and .CSV and get results in the same file formats. Full text search − Solr provides all the capabilities needed for a full text search such as tokens, phrases, spell check, wildcard, and auto-complete. Enterprise ready − According to the need of the organization, Solr can be deployed in any kind of systems (big or small) such as standalone, distributed, cloud, etc. Flexible and Extensible − By extending the Java classes and configuring accordingly, we can customize the components of Solr easily. NoSQL database − Solr can also be used as big data scale NOSQL database where we can distribute the search tasks along a cluster. Admin Interface − Solr provides an easy-to-use, user friendly, feature powered, user interface, using which we can perform all the possible tasks such as manage logs, add, delete, update and search documents. Highly Scalable − While using Solr with Hadoop, we can scale its capacity by adding replicas. Text-Centric and Sorted by Relevance − Solr is mostly used to search text documents and the results are delivered according to the relevance with the user’s query in order. Unlike Lucene, you don’t need to have Java programming skills while working with Apache Solr. It provides a wonderful ready-to-deploy service to build a search box featuring autocomplete, which Lucene doesn’t provide. Using Solr, we can scale, distribute, and manage index, for large scale (Big Data) applications. Lucene in Search Applications Lucene is simple yet powerful Java-based search library. It can be used in any application to add search capability. Lucene is a scalable and high-performance library used to index and search virtually any kind of text. Lucene library provides the core operations which are required by any search application, such as Indexing and Searching. If we have a web portal with a huge volume of data, then we will most probably require a search engine in our portal to extract relevant information from the huge pool of data. Lucene works as the heart of any search application and provides the vital operations pertaining to indexing and searching. Print Page Previous Next Advertisements ”;
Apache Solr – On Hadoop
Apache Solr – On Hadoop ”; Previous Next Solr can be used along with Hadoop. As Hadoop handles a large amount of data, Solr helps us in finding the required information from such a large source. In this section, let us understand how you can install Hadoop on your system. Downloading Hadoop Given below are the steps to be followed to download Hadoop onto your system. Step 1 − Go to the homepage of Hadoop. You can use the link − www.hadoop.apache.org/. Click the link Releases, as highlighted in the following screenshot. It will redirect you to the Apache Hadoop Releases page which contains links for mirrors of source and binary files of various versions of Hadoop as follows − Step 2 − Select the latest version of Hadoop (in our tutorial, it is 2.6.4) and click its binary link. It will take you to a page where mirrors for Hadoop binary are available. Click one of these mirrors to download Hadoop. Download Hadoop from Command Prompt Open Linux terminal and login as super-user. $ su password: Go to the directory where you need to install Hadoop, and save the file there using the link copied earlier, as shown in the following code block. # cd /usr/local # wget http://redrockdigimark.com/apachemirror/hadoop/common/hadoop- 2.6.4/hadoop-2.6.4.tar.gz After downloading Hadoop, extract it using the following commands. # tar zxvf hadoop-2.6.4.tar.gz # mkdir hadoop # mv hadoop-2.6.4/* to hadoop/ # exit Installing Hadoop Follow the steps given below to install Hadoop in pseudo-distributed mode. Step 1: Setting Up Hadoop You can set the Hadoop environment variables by appending the following commands to ~/.bashrc file. export HADOOP_HOME = /usr/local/hadoop export HADOOP_MAPRED_HOME = $HADOOP_HOME export HADOOP_COMMON_HOME = $HADOOP_HOME export HADOOP_HDFS_HOME = $HADOOP_HOME export YARN_HOME = $HADOOP_HOME export HADOOP_COMMON_LIB_NATIVE_DIR = $HADOOP_HOME/lib/native export PATH = $PATH:$HADOOP_HOME/sbin:$HADOOP_HOME/bin export HADOOP_INSTALL = $HADOOP_HOME Next, apply all the changes into the current running system. $ source ~/.bashrc Step 2: Hadoop Configuration You can find all the Hadoop configuration files in the location “$HADOOP_HOME/etc/hadoop”. It is required to make changes in those configuration files according to your Hadoop infrastructure. $ cd $HADOOP_HOME/etc/hadoop In order to develop Hadoop programs in Java, you have to reset the Java environment variables in hadoop-env.sh file by replacing JAVA_HOME value with the location of Java in your system. export JAVA_HOME = /usr/local/jdk1.7.0_71 The following are the list of files that you have to edit to configure Hadoop − core-site.xml hdfs-site.xml yarn-site.xml mapred-site.xml core-site.xml The core-site.xml file contains information such as the port number used for Hadoop instance, memory allocated for the file system, memory limit for storing the data, and size of Read/Write buffers. Open the core-site.xml and add the following properties inside the <configuration>, </configuration> tags. <configuration> <property> <name>fs.default.name</name> <value>hdfs://localhost:9000</value> </property> </configuration> hdfs-site.xml The hdfs-site.xml file contains information such as the value of replication data, namenode path, and datanode paths of your local file systems. It means the place where you want to store the Hadoop infrastructure. Let us assume the following data. dfs.replication (data replication value) = 1 (In the below given path /hadoop/ is the user name. hadoopinfra/hdfs/namenode is the directory created by hdfs file system.) namenode path = //home/hadoop/hadoopinfra/hdfs/namenode (hadoopinfra/hdfs/datanode is the directory created by hdfs file system.) datanode path = //home/hadoop/hadoopinfra/hdfs/datanode Open this file and add the following properties inside the <configuration>, </configuration> tags. <configuration> <property> <name>dfs.replication</name> <value>1</value> </property> <property> <name>dfs.name.dir</name> <value>file:///home/hadoop/hadoopinfra/hdfs/namenode</value> </property> <property> <name>dfs.data.dir</name> <value>file:///home/hadoop/hadoopinfra/hdfs/datanode</value> </property> </configuration> Note − In the above file, all the property values are user-defined and you can make changes according to your Hadoop infrastructure. yarn-site.xml This file is used to configure yarn into Hadoop. Open the yarn-site.xml file and add the following properties in between the <configuration>, </configuration> tags in this file. <configuration> <property> <name>yarn.nodemanager.aux-services</name> <value>mapreduce_shuffle</value> </property> </configuration> mapred-site.xml This file is used to specify which MapReduce framework we are using. By default, Hadoop contains a template of yarn-site.xml. First of all, it is required to copy the file from mapred-site,xml.template to mapred-site.xml file using the following command. $ cp mapred-site.xml.template mapred-site.xml Open mapred-site.xml file and add the following properties inside the <configuration>, </configuration> tags. <configuration> <property> <name>mapreduce.framework.name</name> <value>yarn</value> </property> </configuration> Verifying Hadoop Installation The following steps are used to verify the Hadoop installation. Step 1: Name Node Setup Set up the namenode using the command “hdfs namenode –format” as follows. $ cd ~ $ hdfs namenode -format The expected result is as follows. 10/24/14 21:30:55 INFO namenode.NameNode: STARTUP_MSG: /************************************************************ STARTUP_MSG: Starting NameNode STARTUP_MSG: host = localhost/192.168.1.11 STARTUP_MSG: args = [-format] STARTUP_MSG: version = 2.6.4 … … 10/24/14 21:30:56 INFO common.Storage: Storage directory /home/hadoop/hadoopinfra/hdfs/namenode has been successfully formatted. 10/24/14 21:30:56 INFO namenode.NNStorageRetentionManager: Going to retain 1 images with txid >= 0 10/24/14 21:30:56 INFO util.ExitUtil: Exiting with status 0 10/24/14 21:30:56 INFO namenode.NameNode: SHUTDOWN_MSG: /************************************************************ SHUTDOWN_MSG: Shutting down NameNode at localhost/192.168.1.11 ************************************************************/ Step 2: Verifying the Hadoop dfs The following command is used to start the Hadoop dfs. Executing this command will start your Hadoop file system. $ start-dfs.sh The expected output is as follows − 10/24/14 21:37:56 Starting namenodes on [localhost] localhost: starting namenode, logging to /home/hadoop/hadoop-2.6.4/logs/hadoop- hadoop-namenode-localhost.out localhost: starting datanode, logging to /home/hadoop/hadoop-2.6.4/logs/hadoop- hadoop-datanode-localhost.out Starting secondary namenodes [0.0.0.0] Step 3: Verifying the Yarn Script The following command is used to start the Yarn script. Executing this command will start your Yarn demons. $ start-yarn.sh The expected output as follows − starting yarn daemons starting resourcemanager, logging to /home/hadoop/hadoop-2.6.4/logs/yarn- hadoop-resourcemanager-localhost.out localhost: starting nodemanager, logging to /home/hadoop/hadoop- 2.6.4/logs/yarn-hadoop-nodemanager-localhost.out Step 4: Accessing Hadoop on Browser The default port number to access Hadoop is 50070. Use the following URL to get Hadoop services on browser. http://localhost:50070/ Installing Solr on Hadoop Follow the steps given below to download and install Solr. Step 1 Open the homepage of Apache Solr by clicking the following link − https://lucene.apache.org/solr/ Step 2 Click the download button (highlighted in the above screenshot). On clicking, you will be redirected to the page where you have various mirrors of Apache Solr. Select a mirror and click on it, which will redirect you to a page where
Apache Solr – Core
Apache Solr – Core ”; Previous Next A Solr Core is a running instance of a Lucene index that contains all the Solr configuration files required to use it. We need to create a Solr Core to perform operations like indexing and analyzing. A Solr application may contain one or multiple cores. If necessary, two cores in a Solr application can communicate with each other. Creating a Core After installing and starting Solr, you can connect to the client (web interface) of Solr. As highlighted in the following screenshot, initially there are no cores in Apache Solr. Now, we will see how to create a core in Solr. Using create command One way to create a core is to create a schema-less core using the create command, as shown below − [Hadoop@localhost bin]$ ./Solr create -c Solr_sample Here, we are trying to create a core named Solr_sample in Apache Solr. This command creates a core displaying the following message. Copying configuration to new core instance directory: /home/Hadoop/Solr/server/Solr/Solr_sample Creating new core ”Solr_sample” using command: http://localhost:8983/Solr/admin/cores?action=CREATE&name=Solr_sample&instanceD ir = Solr_sample { “responseHeader”:{ “status”:0, “QTime”:11550 }, “core”:”Solr_sample” } You can create multiple cores in Solr. On the left-hand side of the Solr Admin, you can see a core selector where you can select the newly created core, as shown in the following screenshot. Using create_core command Alternatively, you can create a core using the create_core command. This command has the following options − –c core_name Name of the core you wanted to create -p port_name Port at which you want to create the core -d conf_dir Configuration directory of the port Let’s see how you can use the create_core command. Here, we will try to create a core named my_core. [Hadoop@localhost bin]$ ./Solr create_core -c my_core On executing, the above command creates a core displaying the following message − Copying configuration to new core instance directory: /home/Hadoop/Solr/server/Solr/my_core Creating new core ”my_core” using command: http://localhost:8983/Solr/admin/cores?action=CREATE&name=my_core&instanceD ir = my_core { “responseHeader”:{ “status”:0, “QTime”:1346 }, “core”:”my_core” } Deleting a Core You can delete a core using the delete command of Apache Solr. Let’s suppose we have a core named my_core in Solr, as shown in the following screenshot. You can delete this core using the delete command by passing the name of the core to this command as follows − [Hadoop@localhost bin]$ ./Solr delete -c my_core On executing the above command, the specified core will be deleted displaying the following message. Deleting core ”my_core” using command: http://localhost:8983/Solr/admin/cores?action=UNLOAD&core = my_core&deleteIndex = true&deleteDataDir = true&deleteInstanceDir = true { “responseHeader” :{ “status”:0, “QTime”:170 } } You can open the web interface of Solr to verify whether the core has been deleted or not. Print Page Previous Next Advertisements ”;
Apache Solr – Deleting Documents ”; Previous Next Deleting the Document To delete documents from the index of Apache Solr, we need to specify the ID’s of the documents to be deleted between the <delete></delete> tags. <delete> <id>003</id> <id>005</id> <id>004</id> <id>002</id> </delete> Here, this XML code is used to delete the documents with ID’s 003 and 005. Save this code in a file with the name delete.xml. If you want to delete the documents from the index which belongs to the core named my_core, then you can post the delete.xml file using the post tool, as shown below. [Hadoop@localhost bin]$ ./post -c my_core delete.xml On executing the above command, you will get the following output. /home/Hadoop/java/bin/java -classpath /home/Hadoop/Solr/dist/Solr-core 6.2.0.jar -Dauto = yes -Dc = my_core -Ddata = files org.apache.Solr.util.SimplePostTool delete.xml SimplePostTool version 5.0.0 Posting files to [base] url http://localhost:8983/Solr/my_core/update… Entering auto mode. File endings considered are xml,json,jsonl,csv,pdf,doc,docx,ppt,pptx,xls,xlsx,odt,odp,ods,ott,otp,ots, rtf,htm,html,txt,log POSTing file delete.xml (application/xml) to [base] 1 files indexed. COMMITting Solr index changes to http://localhost:8983/Solr/my_core/update… Time spent: 0:00:00.179 Verification Visit the homepage of the of Apache Solr web interface and select the core as my_core. Try to retrieve all the documents by passing the query “:” in the text area q and execute the query. On executing, you can observe that the specified documents are deleted. Deleting a Field Sometimes we need to delete documents based on fields other than ID. For example, we may have to delete the documents where the city is Chennai. In such cases, you need to specify the name and value of the field within the <query></query> tag pair. <delete> <query>city:Chennai</query> </delete> Save it as delete_field.xml and perform the delete operation on the core named my_core using the post tool of Solr. [Hadoop@localhost bin]$ ./post -c my_core delete_field.xml On executing the above command, it produces the following output. /home/Hadoop/java/bin/java -classpath /home/Hadoop/Solr/dist/Solr-core 6.2.0.jar -Dauto = yes -Dc = my_core -Ddata = files org.apache.Solr.util.SimplePostTool delete_field.xml SimplePostTool version 5.0.0 Posting files to [base] url http://localhost:8983/Solr/my_core/update… Entering auto mode. File endings considered are xml,json,jsonl,csv,pdf,doc,docx,ppt,pptx,xls,xlsx,odt,odp,ods,ott,otp,ots, rtf,htm,html,txt,log POSTing file delete_field.xml (application/xml) to [base] 1 files indexed. COMMITting Solr index changes to http://localhost:8983/Solr/my_core/update… Time spent: 0:00:00.084 Verification Visit the homepage of the of Apache Solr web interface and select the core as my_core. Try to retrieve all the documents by passing the query “:” in the text area q and execute the query. On executing, you can observe that the documents containing the specified field value pair are deleted. Deleting All Documents Just like deleting a specific field, if you want to delete all the documents from an index, you just need to pass the symbol “:” between the tags <query></ query>, as shown below. <delete> <query>*:*</query> </delete> Save it as delete_all.xml and perform the delete operation on the core named my_core using the post tool of Solr. [Hadoop@localhost bin]$ ./post -c my_core delete_all.xml On executing the above command, it produces the following output. /home/Hadoop/java/bin/java -classpath /home/Hadoop/Solr/dist/Solr-core 6.2.0.jar -Dauto = yes -Dc = my_core -Ddata = files org.apache.Solr.util.SimplePostTool deleteAll.xml SimplePostTool version 5.0.0 Posting files to [base] url http://localhost:8983/Solr/my_core/update… Entering auto mode. File endings considered are xml,json,jsonl,csv,pdf,doc,docx,ppt,pptx,xls,xlsx,odt,odp,ods,ott,otp,ots,rtf, htm,html,txt,log POSTing file deleteAll.xml (application/xml) to [base] 1 files indexed. COMMITting Solr index changes to http://localhost:8983/Solr/my_core/update… Time spent: 0:00:00.138 Verification Visit the homepage of Apache Solr web interface and select the core as my_core. Try to retrieve all the documents by passing the query “:” in the text area q and execute the query. On executing, you can observe that the documents containing the specified field value pair are deleted. Deleting all the documents using Java (Client API) Following is the Java program to add documents to Apache Solr index. Save this code in a file with the name UpdatingDocument.java. import java.io.IOException; import org.apache.Solr.client.Solrj.SolrClient; import org.apache.Solr.client.Solrj.SolrServerException; import org.apache.Solr.client.Solrj.impl.HttpSolrClient; import org.apache.Solr.common.SolrInputDocument; public class DeletingAllDocuments { public static void main(String args[]) throws SolrServerException, IOException { //Preparing the Solr client String urlString = “http://localhost:8983/Solr/my_core”; SolrClient Solr = new HttpSolrClient.Builder(urlString).build(); //Preparing the Solr document SolrInputDocument doc = new SolrInputDocument(); //Deleting the documents from Solr Solr.deleteByQuery(“*”); //Saving the document Solr.commit(); System.out.println(“Documents deleted”); } } Compile the above code by executing the following commands in the terminal − [Hadoop@localhost bin]$ javac DeletingAllDocuments [Hadoop@localhost bin]$ java DeletingAllDocuments On executing the above command, you will get the following output. Documents deleted Print Page Previous Next Advertisements ”;
Apache Solr – Indexing Data
Apache Solr – Indexing Data ”; Previous Next In general, indexing is an arrangement of documents or (other entities) systematically. Indexing enables users to locate information in a document. Indexing collects, parses, and stores documents. Indexing is done to increase the speed and performance of a search query while finding a required document. Indexing in Apache Solr In Apache Solr, we can index (add, delete, modify) various document formats such as xml, csv, pdf, etc. We can add data to Solr index in several ways. In this chapter, we are going to discuss indexing − Using the Solr Web Interface. Using any of the client APIs like Java, Python, etc. Using the post tool. In this chapter, we will discuss how to add data to the index of Apache Solr using various interfaces (command line, web interface, and Java client API) Adding Documents using Post Command Solr has a post command in its bin/ directory. Using this command, you can index various formats of files such as JSON, XML, CSV in Apache Solr. Browse through the bin directory of Apache Solr and execute the –h option of the post command, as shown in the following code block. [Hadoop@localhost bin]$ cd $SOLR_HOME [Hadoop@localhost bin]$ ./post -h On executing the above command, you will get a list of options of the post command, as shown below. Usage: post -c <collection> [OPTIONS] <files|directories|urls|-d [“..”]> or post –help collection name defaults to DEFAULT_SOLR_COLLECTION if not specified OPTIONS ======= Solr options: -url <base Solr update URL> (overrides collection, host, and port) -host <host> (default: localhost) -p or -port <port> (default: 8983) -commit yes|no (default: yes) Web crawl options: -recursive <depth> (default: 1) -delay <seconds> (default: 10) Directory crawl options: -delay <seconds> (default: 0) stdin/args options: -type <content/type> (default: application/xml) Other options: -filetypes <type>[,<type>,…] (default: xml,json,jsonl,csv,pdf,doc,docx,ppt,pptx,xls,xlsx,odt,odp,ods,ott,otp,ots, rtf,htm,html,txt,log) -params “<key> = <value>[&<key> = <value>…]” (values must be URL-encoded; these pass through to Solr update request) -out yes|no (default: no; yes outputs Solr response to console) -format Solr (sends application/json content as Solr commands to /update instead of /update/json/docs) Examples: * JSON file:./post -c wizbang events.json * XML files: ./post -c records article*.xml * CSV file: ./post -c signals LATEST-signals.csv * Directory of files: ./post -c myfiles ~/Documents * Web crawl: ./post -c gettingstarted http://lucene.apache.org/Solr -recursive 1 -delay 1 * Standard input (stdin): echo ”{commit: {}}” | ./post -c my_collection – type application/json -out yes –d * Data as string: ./post -c signals -type text/csv -out yes -d $”id,valuen1,0.47” Example Suppose we have a file named sample.csv with the following content (in the bin directory). Student ID First Name Lasst Name Phone City 001 Rajiv Reddy 9848022337 Hyderabad 002 Siddharth Bhattacharya 9848022338 Kolkata 003 Rajesh Khanna 9848022339 Delhi 004 Preethi Agarwal 9848022330 Pune 005 Trupthi Mohanty 9848022336 Bhubaneshwar 006 Archana Mishra 9848022335 Chennai The above dataset contains personal details like Student id, first name, last name, phone, and city. The CSV file of the dataset is shown below. Here, you must note that you need to mention the schema, documenting its first line. id, first_name, last_name, phone_no, location 001, Pruthvi, Reddy, 9848022337, Hyderabad 002, kasyap, Sastry, 9848022338, Vishakapatnam 003, Rajesh, Khanna, 9848022339, Delhi 004, Preethi, Agarwal, 9848022330, Pune 005, Trupthi, Mohanty, 9848022336, Bhubaneshwar 006, Archana, Mishra, 9848022335, Chennai You can index this data under the core named sample_Solr using the post command as follows − [Hadoop@localhost bin]$ ./post -c Solr_sample sample.csv On executing the above command, the given document is indexed under the specified core, generating the following output. /home/Hadoop/java/bin/java -classpath /home/Hadoop/Solr/dist/Solr-core 6.2.0.jar -Dauto = yes -Dc = Solr_sample -Ddata = files org.apache.Solr.util.SimplePostTool sample.csv SimplePostTool version 5.0.0 Posting files to [base] url http://localhost:8983/Solr/Solr_sample/update… Entering auto mode. File endings considered are xml,json,jsonl,csv,pdf,doc,docx,ppt,pptx,xls,xlsx,odt,odp,ods,ott,otp,ots,rtf, htm,html,txt,log POSTing file sample.csv (text/csv) to [base] 1 files indexed. COMMITting Solr index changes to http://localhost:8983/Solr/Solr_sample/update… Time spent: 0:00:00.228 Visit the homepage of Solr Web UI using the following URL − http://localhost:8983/ Select the core Solr_sample. By default, the request handler is /select and the query is “:”. Without doing any modifications, click the ExecuteQuery button at the bottom of the page. On executing the query, you can observe the contents of the indexed CSV document in JSON format (default), as shown in the following screenshot. Note − In the same way, you can index other file formats such as JSON, XML, CSV, etc. Adding Documents using the Solr Web Interface You can also index documents using the web interface provided by Solr. Let us see how to index the following JSON document. [ { “id” : “001”, “name” : “Ram”, “age” : 53, “Designation” : “Manager”, “Location” : “Hyderabad”, }, { “id” : “002”, “name” : “Robert”, “age” : 43, “Designation” : “SR.Programmer”, “Location” : “Chennai”, }, { “id” : “003”, “name” : “Rahim”, “age” : 25, “Designation” : “JR.Programmer”, “Location” : “Delhi”, } ] Step 1 Open Solr web interface using the following URL − http://localhost:8983/ Step 2 Select the core Solr_sample. By default, the values of the fields Request Handler, Common Within, Overwrite, and Boost are /update, 1000, true, and 1.0 respectively, as shown in the following screenshot. Now, choose the document format you want from JSON, CSV, XML, etc. Type the document to be indexed in the text area and click the Submit Document button, as shown in the following screenshot. Adding Documents using Java Client API Following is the Java program to add documents to Apache Solr index. Save this code in a file with the name AddingDocument.java. import java.io.IOException; import org.apache.Solr.client.Solrj.SolrClient; import org.apache.Solr.client.Solrj.SolrServerException; import org.apache.Solr.client.Solrj.impl.HttpSolrClient; import org.apache.Solr.common.SolrInputDocument; public class AddingDocument { public static void main(String args[]) throws Exception { //Preparing the Solr client String urlString = “http://localhost:8983/Solr/my_core”; SolrClient Solr = new HttpSolrClient.Builder(urlString).build(); //Preparing the Solr document SolrInputDocument doc = new SolrInputDocument(); //Adding fields to the document doc.addField(“id”, “003”); doc.addField(“name”, “Rajaman”); doc.addField(“age”,”34″); doc.addField(“addr”,”vishakapatnam”); //Adding the document to Solr Solr.add(doc); //Saving the changes Solr.commit(); System.out.println(“Documents added”); } } Compile the above code by executing the following commands in the terminal − [Hadoop@localhost bin]$ javac AddingDocument [Hadoop@localhost bin]$ java AddingDocument
Apache Solr – Updating Data
Apache Solr – Updating Data ”; Previous Next Updating the Document Using XML Following is the XML file used to update a field in the existing document. Save this in a file with the name update.xml. <add> <doc> <field name = “id”>001</field> <field name = “first name” update = “set”>Raj</field> <field name = “last name” update = “add”>Malhotra</field> <field name = “phone” update = “add”>9000000000</field> <field name = “city” update = “add”>Delhi</field> </doc> </add> As you can observe, the XML file written to update data is just like the one which we use to add documents. But the only difference is we use the update attribute of the field. In our example, we will use the above document and try to update the fields of the document with the id 001. Suppose the XML document exists in the bin directory of Solr. Since we are updating the index which exists in the core named my_core, you can update using the post tool as follows − [Hadoop@localhost bin]$ ./post -c my_core update.xml On executing the above command, you will get the following output. /home/Hadoop/java/bin/java -classpath /home/Hadoop/Solr/dist/Solr-core 6.2.0.jar -Dauto = yes -Dc = my_core -Ddata = files org.apache.Solr.util.SimplePostTool update.xml SimplePostTool version 5.0.0 Posting files to [base] url http://localhost:8983/Solr/my_core/update… Entering auto mode. File endings considered are xml,json,jsonl,csv,pdf,doc,docx,ppt,pptx,xls,xlsx,odt,odp,ods,ott,otp,ots,rtf, htm,html,txt,log POSTing file update.xml (application/xml) to [base] 1 files indexed. COMMITting Solr index changes to http://localhost:8983/Solr/my_core/update… Time spent: 0:00:00.159 Verification Visit the homepage of Apache Solr web interface and select the core as my_core. Try to retrieve all the documents by passing the query “:” in the text area q and execute the query. On executing, you can observe that the document is updated. Updating the Document Using Java (Client API) Following is the Java program to add documents to Apache Solr index. Save this code in a file with the name UpdatingDocument.java. import java.io.IOException; import org.apache.Solr.client.Solrj.SolrClient; import org.apache.Solr.client.Solrj.SolrServerException; import org.apache.Solr.client.Solrj.impl.HttpSolrClient; import org.apache.Solr.client.Solrj.request.UpdateRequest; import org.apache.Solr.client.Solrj.response.UpdateResponse; import org.apache.Solr.common.SolrInputDocument; public class UpdatingDocument { public static void main(String args[]) throws SolrServerException, IOException { //Preparing the Solr client String urlString = “http://localhost:8983/Solr/my_core”; SolrClient Solr = new HttpSolrClient.Builder(urlString).build(); //Preparing the Solr document SolrInputDocument doc = new SolrInputDocument(); UpdateRequest updateRequest = new UpdateRequest(); updateRequest.setAction( UpdateRequest.ACTION.COMMIT, false, false); SolrInputDocument myDocumentInstantlycommited = new SolrInputDocument(); myDocumentInstantlycommited.addField(“id”, “002”); myDocumentInstantlycommited.addField(“name”, “Rahman”); myDocumentInstantlycommited.addField(“age”,”27″); myDocumentInstantlycommited.addField(“addr”,”hyderabad”); updateRequest.add( myDocumentInstantlycommited); UpdateResponse rsp = updateRequest.process(Solr); System.out.println(“Documents Updated”); } } Compile the above code by executing the following commands in the terminal − [Hadoop@localhost bin]$ javac UpdatingDocument [Hadoop@localhost bin]$ java UpdatingDocument On executing the above command, you will get the following output. Documents updated Print Page Previous Next Advertisements ”;
Apache Solr – Querying Data
Apache Solr – Querying Data ”; Previous Next In addition to storing data, Apache Solr also provides the facility of querying it back as and when required. Solr provides certain parameters using which we can query the data stored in it. In the following table, we have listed down the various query parameters available in Apache Solr. Parameter Description q This is the main query parameter of Apache Solr, documents are scored by their similarity to terms in this parameter. fq This parameter represents the filter query of Apache Solr the restricts the result set to documents matching this filter. start The start parameter represents the starting offsets for a page results the default value of this parameter is 0. rows This parameter represents the number of the documents that are to be retrieved per page. The default value of this parameter is 10. sort This parameter specifies the list of fields, separated by commas, based on which the results of the query is to be sorted. fl This parameter specifies the list of the fields to return for each document in the result set. wt This parameter represents the type of the response writer we wanted to view the result. You can see all these parameters as options to query Apache Solr. Visit the homepage of Apache Solr. On the left-hand side of the page, click on the option Query. Here, you can see the fields for the parameters of a query. Retrieving the Records Assume we have 3 records in the core named my_core. To retrieve a particular record from the selected core, you need to pass the name and value pairs of the fields of a particular document. For example, if you want to retrieve the record with the value of the field id, you need to pass the name-value pair of the field as − Id:001 as value for the parameter q and execute the query. In the same way, you can retrieve all the records from an index by passing *:* as a value to the parameter q, as shown in the following screenshot. Retrieving from the 2nd record We can retrieve the records from the second record by passing 2 as a value to the parameter start, as shown in the following screenshot. Restricting the Number of Records You can restrict the number of records by specifying a value in the rows parameter. For example, we can restrict the total number of records in the result of the query to 2 by passing the value 2 into the parameter rows, as shown in the following screenshot. Response Writer Type You can get the response in required document type by selecting one from the provided values of the parameter wt. In the above instance, we have chosen the .csv format to get the response. List of the Fields If we want to have particular fields in the resulted documents, we need to pass the list of the required fields, separated by commas, as a value to the property fl. In the following example, we are trying to retrieve the fields − id, phone, and first_name. Print Page Previous Next Advertisements ”;
Apache Solr – Retrieving Data ”; Previous Next In this chapter, we will discuss how to retrieve data using Java Client API. Suppose we have a .csv document named sample.csv with the following content. 001,9848022337,Hyderabad,Rajiv,Reddy 002,9848022338,Kolkata,Siddarth,Battacharya 003,9848022339,Delhi,Rajesh,Khanna You can index this data under the core named sample_Solr using the post command. [Hadoop@localhost bin]$ ./post -c Solr_sample sample.csv Following is the Java program to add documents to Apache Solr index. Save this code in a file with named RetrievingData.java. import java.io.IOException; import org.apache.Solr.client.Solrj.SolrClient; import org.apache.Solr.client.Solrj.SolrQuery; import org.apache.Solr.client.Solrj.SolrServerException; import org.apache.Solr.client.Solrj.impl.HttpSolrClient; import org.apache.Solr.client.Solrj.response.QueryResponse; import org.apache.Solr.common.SolrDocumentList; public class RetrievingData { public static void main(String args[]) throws SolrServerException, IOException { //Preparing the Solr client String urlString = “http://localhost:8983/Solr/my_core”; SolrClient Solr = new HttpSolrClient.Builder(urlString).build(); //Preparing Solr query SolrQuery query = new SolrQuery(); query.setQuery(“*:*”); //Adding the field to be retrieved query.addField(“*”); //Executing the query QueryResponse queryResponse = Solr.query(query); //Storing the results of the query SolrDocumentList docs = queryResponse.getResults(); System.out.println(docs); System.out.println(docs.get(0)); System.out.println(docs.get(1)); System.out.println(docs.get(2)); //Saving the operations Solr.commit(); } } Compile the above code by executing the following commands in the terminal − [Hadoop@localhost bin]$ javac RetrievingData [Hadoop@localhost bin]$ java RetrievingData On executing the above command, you will get the following output. {numFound = 3,start = 0,docs = [SolrDocument{id=001, phone = [9848022337], city = [Hyderabad], first_name = [Rajiv], last_name = [Reddy], _version_ = 1547262806014820352}, SolrDocument{id = 002, phone = [9848022338], city = [Kolkata], first_name = [Siddarth], last_name = [Battacharya], _version_ = 1547262806026354688}, SolrDocument{id = 003, phone = [9848022339], city = [Delhi], first_name = [Rajesh], last_name = [Khanna], _version_ = 1547262806029500416}]} SolrDocument{id = 001, phone = [9848022337], city = [Hyderabad], first_name = [Rajiv], last_name = [Reddy], _version_ = 1547262806014820352} SolrDocument{id = 002, phone = [9848022338], city = [Kolkata], first_name = [Siddarth], last_name = [Battacharya], _version_ = 1547262806026354688} SolrDocument{id = 003, phone = [9848022339], city = [Delhi], first_name = [Rajesh], last_name = [Khanna], _version_ = 1547262806029500416} Print Page Previous Next Advertisements ”;
Apache Solr – Architecture
Apache Solr – Architecture ”; Previous Next In this chapter, we will discuss the architecture of Apache Solr. The following illustration shows a block diagram of the architecture of Apache Solr. Solr Architecture ─ Building Blocks Following are the major building blocks (components) of Apache Solr − Request Handler − The requests we send to Apache Solr are processed by these request handlers. The requests might be query requests or index update requests. Based on our requirement, we need to select the request handler. To pass a request to Solr, we will generally map the handler to a certain URI end-point and the specified request will be served by it. Search Component − A search component is a type (feature) of search provided in Apache Solr. It might be spell checking, query, faceting, hit highlighting, etc. These search components are registered as search handlers. Multiple components can be registered to a search handler. Query Parser − The Apache Solr query parser parses the queries that we pass to Solr and verifies the queries for syntactical errors. After parsing the queries, it translates them to a format which Lucene understands. Response Writer − A response writer in Apache Solr is the component which generates the formatted output for the user queries. Solr supports response formats such as XML, JSON, CSV, etc. We have different response writers for each type of response. Analyzer/tokenizer − Lucene recognizes data in the form of tokens. Apache Solr analyzes the content, divides it into tokens, and passes these tokens to Lucene. An analyzer in Apache Solr examines the text of fields and generates a token stream. A tokenizer breaks the token stream prepared by the analyzer into tokens. Update Request Processor − Whenever we send an update request to Apache Solr, the request is run through a set of plugins (signature, logging, indexing), collectively known as update request processor. This processor is responsible for modifications such as dropping a field, adding a field, etc. Print Page Previous Next Advertisements ”;