If you are not looking at your company’s operational logs, then you are at a competitive disadvantage in your industry. In the past, it is cost-prohibitive to capture all logs, let alone implement systems that act on them intelligently in real time. Flume, Solr, Hue, and Kafka can all be easily installed using Cloudera Manager and parcels (the first three via the CDH parcel, and Kafka via its own parcel).
The high-level diagram below illustrates a simple setup that you can deploy in a matter of minutes. Every time you start a new project that involves Solr, you must first understand your data and organize it into fields.
Cloudera Search, which is integrates Solr with HDFS, is deployed in SolrCloud mode with all the options and flexibility that come with integrating to the rest of the Hadoop ecosystem in CDH.
Although you are not using the id and _version_ fields in this application, Solr uses them internally for its own bookkeeping. Again, replace –zk localhost:2181 with your own ZooKeeper quorum configuration in both statements. Before you index the logs for searching, you need to collect them from the application servers. As previously described, our example uses Flume with Syslog Source to collect the log data from syslog, Kafka as a distributed and highly available channel to store the log data, and Solr sink with Morphlines to index the data and store it in Cloudera Search. Next, a Solr sink, configured with a configuration file that we’ll review in detail later.
All this is nice if the data is arriving from syslog and going only to Solr by way of Morphlines, but in today’s enterprise IT, there are usually many different data sources. To get data from Kafka, parse it with Morphlines, and index it into Solr, you can use an almost identical configuration. These changes are necessary because the events are now written to Kafka by apps other than Flume, so the source is not necessary (Kafka channel will get events from Kafka to SolrSink) and the events in the channel can be any data type, not necessarily a FlumeEvent. This configuration allows indexing and searching using Cloudera Search any enterprise event that was written to Kafka (including logs, metrics, audit events, and so on).
Our morphline configuration file will break down raw apache logs and generate Solr fields that will be used for indexing. When building this example, we initially used three morphlines commands to break up the Apache log event: readCSV, split, split. Solr is an open-source, enterprise-level search platform, from the Apache Lucene project, that is known for scalability and performance. NOTE: To compare Solr with other Ektron search providers, see Search Provider Comparison Table. The Ektron Solr search provider is made up of several components, which can crawl your content to index the latest changes, as well as manage search queries. The Apache ManifoldCF (connector framework) crawls data from a repository connector to an output connector.
An Ektron repository connection is created, using database information from the Ektron site. A Solr output connection is created with a connection to a Solr core exclusive to the Ektron site. Ektron's crawl component uses the Asset Transfer Service as the endpointpart of a DXH connection, it is the combination of an endpoint (a URL or API path) and authentication credentials that allows communication between 2 software instances. When the crawl service requires an asset, it asks the Ektron Asset Transfer Client to retrieve it. The Query Service routes queries from sites registered with the Solr Search server to the Solr core that corresponds to the site. The Query Proposition service is a RESTful service that routes autocomplete queries to the Solr core that corresponds to the site. The Search Config database serves as a "centralized store." It maintains configuration information for search endpoints, Solr, ManifoldCF, Admin Service, and registration information for the registered sites. A Solr core is a single instance of Solr with its own configuration, schema, and independent index. Ektron's search deployment uses handlers to manage queries (search queries & completion requests), administrative requests (to manage cores), and content updating. Solr's ExtractingRequestHandler uses Tika to lets users upload binary files to Solr, and then have Solr extract text from and index them. Keyword search isthe search performed when a user types text or a phrase into a text field. Content title and body are subject to linguistic processing, which applies rules based on the language of the search text or phrase. Stemming helps a user find desired content because, in most cases, the user is unsure of the exact text in the document. Stop words are terms that are removed from queries and indexes because they do not contribute to the quality of a search.
When ranking content returned as search results, Solr uses the quality of a document's match against query text.
If search terms include an OR operator, content with the greatest number of terms ranks highest. The EqualsExpression, on the other hand, performs a letter to letter, case-sensitive, exact search on the entire text of a field. To continue the above example for an EqualsExpression, a match results only if the query is submitted as a€?Tax Revenue Statement, Year 2012a€?. The Ektron Solr package has the Java Heap Memory (HSQLDB-Crawl Database, ManifoldCF-Crawler, and Tomcat-Solr Process) tuned to support the simultaneous registration and crawling of multiple sites. Because the Solr configuration is less reliant on the Java Heap for loading the index , the default tuning should suffice for most production usages.
Query suggestions are a search type-ahead feature, in which a search field presents query options as the user begins to type in characters. Query completions are another search type-ahead feature in which the search field presents query options as a user begins to type in characters. You can change the source of query completions from the search index to a dictionary of terms. With regards to query completions, the search only considers terms from the title by default.
On the Solr search server's firewall, open to the Ektron Web server ports +0 and +1 in the rangea€”they are used by Ektrona€™s query and admin services. Enter credentials (domain, user name, and password) of the Windows user account under which Solr query services will run. Enter credentials for a SQL login account that Ektron search components will use to access the search configuration database.

The site registration process automates these tasks, necessary to prepare a sitea€™s index. The screen also lets you set or modify search information, such as the incremental crawl intervalWhenever a crawl finishes, Ektron begins to track the time. This section explains how to set up Solr search to support an Ektron website deployed to the Amazon cloud.
The security group authorizes the opening of ports, which access search components between Ektron and the Solr VM. NOTE: Create the Security Group for the region you selected for the Cloud site (for example, US East).
On the Configure Instance screen, check Protect against accidental termination and click Next.
Part 1 of this post covered how to convert and store email messages for archival purposes using Apache Hadoop, and outlined how to perform a rudimentary search through those archives. Apache Lucene is a mature, high performance, full-featured Java API used for indexing and searching that has been around since the late nineties — it supports field-specific indexing and searching, sorting, highlighting, and wildcard searches, to name only a few. Apache Solr, on the other hand, is a Lucene-based full text search server with XML, JSON, and HTTP APIs, which has a web admin interface and provides extensive caching, replication, search distribution, as well as the ability to add customized plugins.
First, you need to get data into HDFS, as covered in Hadoop for Archiving Email – Part 1. Having discussed design at a high level, let’s now dive deeper into the details of MapReduce for creating an index.
If writing to HDFS, you can use RAMDirectory to hold the indexes created; and once complete, flush to HDFS. Once it’s configured within each map, the exercise boils down to parsing the content and adding it to the writer.
The index should now have been created, either in the Local File System of each of the DataNodes, or in HDFS directly. Since we are discussing fast search options, it also makes sense to touch on components like SolrCloud and Katta. SolrCloud enables clusters of Solr instances to be created, with a central configuration, automatic load balancing, resizing, rebalancing and fail-over.
In Part 3 of this series, I will cover ways to ingest such email messages and ways to put the steps involved in a workflow.
This tutorial explains how to index the Apache Log into Solr and start doing your own analytics. After the installation, When I try to add the host I get the following error: Could not connect to host.
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This simple use case illustrates how to make web log analysis, powered in part by Kafka, one of your first steps in a pervasive analytics journey. Web server logs, application logs, and system logs are all valuable sources of operational intelligence, uncovering potential revenue opportunities and helping drive down the bottom line.
Recently, however, technology has matured quite a bit and, today, we have all the right ingredients we need in the Apache Hadoop ecosystem to capture the events in real time, process them, and make intelligent decisions based on that information.
This implementation is based on open source components such as Apache Flume, Apache Kafka, Hue, and Apache Solr. Fortunately, Apache web server logs are easy enough to understand and relate to Solr documents. The most important and the only file to update is schema.xml, which is used in Solr to define the fields in the collection, their types, and their indexing characteristics. Thus you can use this setting to add scalability via multiple Flume agents, each with a Kafka channel configured with the same groupId. Thus the first time the channel starts, all events in the topic are read (subsequently, only the last recorded position is read).
In many companies, applications write important events directly to Kafka without going through syslog or Log4J at all. Apache log parsing is achieved with the help of the Morphlines library, an open source framework available through the Kite SDK, that defines a transformation chain without a single line of code. Our intention was to make this blog more generic and demonstrate how easy it can be adapted to all different types of logs. In its implementation of Solr, Ektron uses Apache Tomcat to host the Solr application, and ManifoldCF to manage the crawling of new or updated content.
Ektron's Solr implementation supports advanced search features, such as Refinements, Synonym Sets, Suggested Results, and Autocomplete. Solr manages the search for any number of Ektron instances, each of which can support several sites.
Ektron's Solr deployment uses its own repository connector with the Solr output connector that updates the Solr Index.
If the Ektron site and Solr run on the same server, the client directs the indexer to the asset's location on the file system. For PageBuilder pages, content body corresponds to the content table's content_text column.
For example, the terms "engineering" and "engine" are both reduced to "engine" during indexing. Other fields specified in the ExpressionTree property are not considered for rank computation. For example, if a text metadata field contains "Tax Revenue Statement, Year 2012," a contains search returns the content if a query using ContainsExpression includes "Tax" or "Revenue Statement" or "Revenue" as whole words.
This tuning is optimized for a server with at least 8GB RAM and 2 high speed disks (1 for the search index, and 1 for search installation). However, in this case, query options are based on either the search index (the default option) or a dictionary of terms.
Then either select Trusted Connection or enter the username and password of the user who will create the search configuration database.
After the number of seconds specified in the Incremental Crawl Interval expires, Ektron checks for changes to the database. When registration is complete, the request is submitted to the Search Administration console.
But, let’s face it: for search to be of any real value, you need robust features and a fast response time. Solr already includes various parsing libraries, including Tika, POI, and TagSoup, among others.

Once the data is there, you can start to run MapReduce to create indexes in parallel that can then be dumped into HDFS or into a Local File System. Luke was built for development and diagnostic purposes, and can be used to search, display and browse the results of a Lucene index.
If it is in the Local File System, you can opt to make the directory part of the “www” directory and enable Solr to serve it from there. If the index sits in a Local File System, this can be accomplished by setting the index writer to APPEND mode and adding new documents. It has built in replication for fail-over and performance, is easy to integrate with Hadoop clusters and has master fail-over.
In the meantime, drop us a line if you have any questions on storing email message in Hadoop and index and search them using Solr and Lucene. The user experience has been greatly improved, as the app now provides a very easy way to build custom dashboards and visualizations. Lucid Imagination created an integrated Search-as-a-Service platform that simplifies and empowers predictable, reliable search application development.
Whether your firm is an advertising agency that analyzes clickstream logs for customer insight, or you are responsible for protecting the firm’s information assets by preventing cyber-security threats, you should strive to get the most value from your data as soon as possible.
For example, if your syslog index is distributed across multiple Solr Instances, they all add up to form one collection. The second command creates the collection in Solr, based on the configuration in ZooKeeper from the first command. For example, if you have a corpus of 1 million events, you may want to split it into two shards for scalability and improved query performance. Or, if you need multiple channels that all receive all the data from Kafka (essentially duplicating all the data), you’ll want to use different groupIds or different topics. However, the creators of the morphlines library have generously provided a number of pre-defined patterns for commonly used log formats, including Apache web server ones. If the asset resides on a remote server, the client requests the asset from the appropriate Ektron Asset Transfer Server and stores it in cache for indexing.
For Solr-based search, linguistic processing refers to stemming and stop word recognition only.
During querying, the language of the query (by default, the site language) is used to perform stemming. The filter query merely helps in reduction of the result subset and does not improve relevance.
To avoid this slow first response, create a dummy search request to be run after a site is registered for the first time. Ektron does not recommend installing Solr on a server that also hosts Microsoft Search Server.Roles and features. For example, if you use the default value of 7600, ports 7600 through 7609 are dedicated to Solr search.
The account must have permission towrite to the search 1.0 folder and its subfoldersstart the Solr admin servicelog on as a service.
Set up the Ektron server's firewall such that these ports let the Ektron site communicate with the Solr service. This connection is made from the Solr server, so be sure that the Solr server can resolve the Ektron server as a host name.
When extracting content from .msg email files, for instance, TIKA and POI are some useful libraries. If an index is stored within a Local File System, simply serve it from there by pointing Solr to it. With Luke, you can view documents, analyze results, copy and delete them, or optimize indexes that have already been built.
If it is in HDFS, one could load the index in RAMDirectory within each mapper and search, use a tool like Luke to provide a search interface, or put a mechanism in place to copy it to the Local File System to point Solr at it. However, it does not provide real-time updates, nor is it an indexer – it is simply a serving tool for Lucene indexes.
Syslog Source sends them to Kafka Channel, which in turn passes them to a MorphlineSolr sink. The first shard might handle all the documents that have an id between 0-500,000, and the second shard will handle documents with message id between 500,000-1,000,000.
In addition, only events written after the channel started are read (since the topic may have a large history in it already). So if you previously worked with these components, you only need to master a few new concepts to migrate to Solr.
The Ektron Asset Transfer Server, which resides on the Ektron Web server, is one half of a pair of services that supports the indexing of assets. Keyword search is performed against the content title and the body only, because they contain the "meat" of a content item.
Also, since Solr only performs stemming reduction, a query containing "ran" does not match a document containing "running", since these words get reduced to "ran" (query side) and "run"(index time).
You can also avoid this slow first response by issuing a search request from a site (for every site) after the Solr search server has been rebooted.
It can be run either in single, replicated or distributed mode, depending on the size of the index to serve.  However, if you need to make search available for only for a small number of users, you can simply store data directly in  HDFS and provide an interface for your users to access it directly. One option would be to write an index to a new directory in HDFS, then merge with the existing index. MorphlineSink parses the messages, converts them into Solr documents, and sends them to Solr Server. Solr handles all this logic internally; you need only specify the number of shards you would like to create with the -s option. Although Integrated Security is supported, the connection is made in the context of the search service account (Windows user) provided during installation. After the indexed documents appear in Solr, Hue’s Search Application is utilized to search the indexes and build and display multiple unique dashboards for various audiences.
If this user does not have access to the database, the connection fails.Crawl Filtersa€”Determine the types of content to be crawled.

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