Lucene has a couple classes to help you get started with distributed search, and Solr provides a simple, full blown solution that can scale to billions of documents. Lucene provides a RemoteSearchable implementation that allows for distributed search with either a MultiSearcher or more likely a ParallelMultiSearcher. Rather than search a handful of local Searchables with a MultiSeacher, you can use the MultiSearcher to search across a number of RemoteSearchables, each pointing to a different server.
Just as with a local MultiSearcher search, each sub Searchable will be searched, and the results combined. This method of scaling has been used for many distributed setups, but it is not an ideal solution and suffers from excessive chatter between servers, stunting truly large scale scalability. As is often the case, it might be best to look at Solr for best practices in distributing Lucene.
Keep in mind that there are other approaches out there and in use. Solr provides an extremely simple, extremely scalable, distributed solution out of the box. As I mentioned in the introduction, Lucene is killer core IR technology, and Solr is a search server built on top with some of its own killer technology — see faceting in particular.
Solr includes deceptively simple distributed support built on top of Lucene. Building a distributed Solr server farm is as simple as installing Solr on each machine. There is no out of the box support for distributed indexing, but your method can be as simple as a round robin technique: Index each document to the next server in the circle.
A simple hashing system would also work, and the Solr Wiki suggests uniqueId. Its probably best to randomly distribute documents to your shards. Once you have your documents indexed to each shard, searching across multiple shards is dead simple:. You simply add a shards parameter that contains each shards URL, comma separated. This will cause the select RequestHandler to search each of the listed URLs indepently and then combine the results as if you had issued one search across one large index.
You should load balance requests across each of the servers. That way you can set it once and effectively forget about it for a while. The current components that support distributed search are:. For best results, you will want to load balance incoming requests across each of the shards. Each request that hits a shard will be distributed by that shard to itself and the other shards and then the results are merged. You want to be sure to distribute that duty evenly across your shards.
Be careful of the deadlock warning in the Solr Wiki if you do this though. You need to be sure that the number of threads serving http requests in your container is greater than the number of requests you can get from the shard itself, and all of the other shards in your configuration, or you may experience a deadlock. Get the full details on setting up distributed search with Solr at the Solr Wiki. The idea is to combine distributed search with replication.
Take a look at the Distributed and Replicated figure. This allows the master to handle updates and optimizations without adversely affecting query handling performance. Query requests should be load balanced across each of the shard slaves. This gives you both increased query handling capacity and fail over backup if a server goes down. With distribution and replication, none of the master shards know about each other.
If you are new to load balancing, HAProxy is a good open source software load balancer. If a slave server goes down, a good load balancer will detect the failure using some technique generally a heartbeat system , and forward all requests to the remaining live slaves that served with the failed slave.
A single virtual IP should then be set up so that requests can hit a single IP, and get load balanced to each of the virtual IPs for the search slaves. With this configuration you will have a fully load balanced, search side fault tolerant system Solr does not yet support fault tolerant indexing. Incoming searches will be handed off to one of the functioning slaves, then the slave will distribute the search request across a slave for each of the shards in your configuration.
The slave will issue a request to each of the virtual IPs for each shard, and the load balancer will choose one of the available slaves. Finally the results will be combined into a single results set and returned. If any of the slaves go down, they will be taken out of rotation and the remaining slaves will be used. If a shard master goes down, searches can still be served from the slaves until you have corrected the problem and put the master back into production. For most applications, if you start developing a scalable solution with Lucene, you begin to build a home brew search engine.
This is usually not wise. Lucene attempts to be more of a toolkit, while Solr looks to be more of an end-to-end search solution. So why talk about scaling Lucene as well as Solr? You might need to scale Lucene if you inherit legacy code or have specific requirements that prevent you from using Solr.
In general though, there is a fair amount of work involved to scale Lucene properly across multiple machines. Solr has done much of this, as well as a lot of other higher level work, and it is wise to take advantage of it. Remember, Lucene provides the tools to build a highly scalable search solution, while the Lucene sub project, Solr, uses Lucene to build such a solution.
Hopefully, you now see why I started with maximizing the performance of a single machine. Both replication and distribution effectively turn into individual searches against each individual server which are then combined in the distributed case. Most of the fruitful efforts in maximizing performance for distributed and replicated search are therefore the same as those for maximizing performance on a single machine.
I hope I have shown that Lucene and Solr both prove to be highly scalable search solutions. There is likely still plenty of exploring and testing that you will have to do for your unique requirements when it comes to a large scale installation, but hopefully you now have a little more direction for your journey.
With the proper configuration, scaling from millions to billions of documents with sub second response times, even under high load and reliability requirements, is very achievable. Contact us today to learn how Lucidworks can help your team create powerful search and discovery applications for your customers and employees.
Skip to Main Content. Introduction For the less acquainted, Lucene is a very compact and powerful search library while Solr is an enterprise search engine built on top of the Lucene library. Term Frequencies Depending on your data, many fields can benefit from using Fieldable. Norms Use omitNorms wherever it makes sense. Lazy Field Loading When Lucene and Solr load a Document from the index say for highlighting and hit display , all of the stored fields for that Document are loaded at once.
Stop Words As you approach the upper limits of a single machine, extremely frequent terms called stop words can become very expensive in the wrong query. Index Optimization A Lucene index is made up of 1-n segments. Lucene Caches In a large scale search application, caches can become very important. FieldCache Lucene uses FieldCache to efficiently access all of the values for a field in memory rather than going to disk.
Documents It is also a good idea to cache Lucene Documents. As usual, it helps to look at Solr for best practices when it comes to a Lucene application. Solr Caches Besides custom user caches, Solr has three types of built-in caches. Each cache should be carefully considered: FilterCache — unordered document ids. This is for caching filter queries. This cache stores enough information to filter out the right documents across the whole index for a given query.
Using set intersections on these filtered ids allows for efficiency in combining filter queries. If you are faceting with the FieldCache method and you should be if you have a large number of unique fields , this should be set to at least the number of unique values in all the fields you are using for faceting using the FieldCache method. QueryCache — ordered document ids. This is for caching the results of normal queries. This can require much less RAM than the FilterCache because it only caches the returned documents, while the FilterCache must cache the results for the whole index.
The optimal size of this cache depends on a lot of factors. Essentially, you want to make sure that it is large enough so that the majority of the results of your really common queries are cached. DocumentCache — stores stored fields. Solr caches Documents in memory so that no request has to hit the disk for stored fields. This can be very valuable as stored fields are most often used for hit list displays. Solr Faceting Solr has an excellent and efficient faceting implementation, but it really pays to consider its effects on memory.
FacetQueries are handled by caching the results of a query as a filter. This FacetQuery set of documents is intersected against result sets to count how many documents a query condition is true for the facet counts. If there are few enough results in the filter, the filter is maintained as a hashed set of document ids.
FacetFields allow for facet counts based on distinct values in a field. There are two methods for FacetFields, one that performs well with few distinct values in a field, and the other for when a field contains many distinct values generally, thousands and up — you should test what works best for you.
The first method, facet. As mentioned, this is an excellent method when the number of distinct values in a field is small. It requires excessive memory though, and breaks down when the number of distinct values gets large. When using this method, be careful to ensure that your FilterCache is large enough to contain at least one filter for every distinct value you plan on faceting on. The second method uses the Lucene FieldCache future version of Solr will actually use a different non-inverted structure — the UnInvertedField.
This method is actually slower and more memory intensive for fields with a low number of unique values, but if you have a lot of uniques, this is the way to go. This method uses the FieldCache to look up the values for the given field for each document, and every time a document with a given value is found, the value has its count incremented. Queries You should try to keep in mind which types of queries are generally slower and consider their use carefully.
Maximizing Throughput When you start using Lucene and Solr on a server with many cores or processors, you might start running into certain known bottlenecks. Choosing Xms Xmx One strategy is to set a very low min memory and a high max memory. The rest of your server Ensure that your Solr and Lucene indexes are excluded from any indexing applications Windows indexing service, desktop search apps, etc.
Large Index Sizes Some indexes get so large that a single machine cannot adequately contain them. Distributed Solr Solr provides an extremely simple, extremely scalable, distributed solution out of the box. The current components that support distributed search are: The Query component that returns documents matching a query The Facet component, for facet.
The Highlighting component the Debug component For best results, you will want to load balance incoming requests across each of the shards. Conclusion For most applications, if you start developing a scalable solution with Lucene, you begin to build a home brew search engine. Apache Lucene is a high-performance and full-featured text search engine library written entirely in Java from the Apache Software Foundation.
It is a technology suitable for nearly any application that requires full-text search, especially in a cross-platform environment. In this article, we will see some exciting features of Apache Lucene. A step-by-step example of documents indexing and searching will be shown too. Lucene offers powerful features like scalable and high-performance indexing of the documents and search capability through a simple API.
It utilizes powerful, accurate and efficient search algorithms written in Java. Most importantly, it is a cross-platform solution. Lucene provides search over documents; where a document is essentially a collection of fields. A field consists of a field name that is a string and one or more field values. Lucene does not in any way constrain document structures. Fields are constrained to store only one kind of data, either binary, numeric, or text data. There are two ways to store text data: string fields store the entire item as one string; text fields store the data as a series of tokens.
Lucene provides many ways to break a piece of text into tokens as well as hooks that allow you to write custom tokenizers. Lucene has a highly expressive search API that takes a search query and returns a set of documents ranked by relevancy with documents most similar to the query having the highest score. The Lucene API consists of a core library and many contributed libraries. The top-level package is org. As of now, Lucene 6, the Lucene distribution contains approximately two dozen package-specific jars, these cuts down on the size of an application at a small cost to the complexity of the build file.
In a nutshell, the features of Lucene can be described as follows:. In this section, we will see how does Apache Lucene work towards documents indexing and searching. Lucene manages an index over a dynamic collection of documents and provides very rapid updates to the index as documents are added to and deleted from the collection. An index may store a heterogeneous set of documents, with any number of different fields that may vary by a document in arbitrary ways.
Lucene indexes terms , which means that Lucene search searches over terms. A term combines a field name with a token. The terms created from the non-text fields in the document are pairs consisting of the field name and the field value.
The terms created from text fields are pairs of field name and token. The Lucene index provides a mapping from terms to documents. This is called an inverted index because it reverses the usual mapping of a document to the terms it contains.
The inverted index provides the mechanism for scoring search results: if a number of search terms all map to the same document, then that document is likely to be relevant. Conceptually, Lucene provides indexing and search over documents, but implementation-wise, all indexing and search are carried out over fields. A document is a collection of fields. Each field has three parts: name, type, and value. At search time, the supplied field name restricts the search to particular fields.
Each of these fields is given a different name, and at search time, the client could specify that it was searching for authors or titles or both, potentially restricting to a date range and set of journals by constructing search terms for the appropriate fields and values. Document indexing consists of first constructing a document that contains the fields to be indexed or stored, then adding that document to the index.
The key classes involved in indexing are, oal. IndexWriter which is responsible for adding documents to an index, and, oal.
Directory which is the storage abstraction used for the index itself. A Directory contains any number of sub-indexes called segments. Maintaining the index as a set of segments allows Lucene to rapidly update and delete documents from the index. Defined within an index, an analyzer consists of a single tokenizer and any number of token filters.
For example, a tokenizer could split a string into specifically defined terms when encountering a specific expression. The most common analyzers include the Standard Analyzer and the Simple Analyzer, as well as several language-specific analyzers. By default, Elasticsearch will apply the Standard Analyzer, which contains a grammar-based tokenizer that removes common English words and applies additional filters.
Elasticsearch comes bundled with a series of built-in tokenizers as well, and you can also use a custom tokenizer. A token filter is used to filter or modify some tokens. The heart of any ELK setup is the Elasticsearch instance, which has the crucial task of storing and indexing data.
By default, each node is automatically assigned a unique identifier — or name — that is used for management purposes and becomes even more important in a multi-node, or clustered, environment. All nodes are also capable by default of being master nodes, data nodes ingest nodes or machine learning nodes.
It is recommended to distinguish each node by a single type, especially as clusters grow larger. Needless to say, these nodes need to be able to identify each other to be able to connect. In a development or testing environment, you can set up multiple nodes on a single server.
In production, however, due to the number of resources that an Elasticsearch node consumes, it is recommended to have each Elasticsearch instance run on a separate server. An Elasticsearch cluster is comprised of one or more Elasticsearch nodes. As with nodes , each cluster has a unique identifier that must be used by any node attempting to join the cluster. Be sure not to use the same name for one cluster in different environments, otherwise nodes might be grouped with the wrong cluster.
This node is chosen automatically by the cluster, but it can be changed if it fails. See above on the other types of nodes in a cluster. But nodes also forward queries to the node that contains the data being queried. As a cluster grows, it will reorganize itself to spread the data.
Even though clusters are designed to host multiple nodes, you can assign only node to a cluster if it is so desired. There are a number of useful cluster APIs that can query the general status of the cluster. Read more about cluster APIs here. These are the main concepts you should understand when getting started with ELK, but there are other components and terms as well. One important thing to point about Types is that even though there can be many Types in the same Index, Fields of the same name in different Types must have the same Mapping within an index.
Did they still confuse for this learning curve stage? Platform Overview. Features Alerts. Fully-Managed ELK. About us. About Logz. Free Trial Request Demo Login. Daniel Berman. Fields Fields are the smallest individual unit of data in Elasticsearch. Multi-fields These fields can and should be indexed in more than one way to produce more search results.
Documents Documents are JSON objects that are stored within an Elasticsearch index and are considered the base unit of storage. Mapping As far as mapping goes, bear in mind that since Elasticsearch 7.
Mapping Types Not to be confused with datatypes , mapping types are now a legacy aspect of Elasticsearch, related to all previous versions released prior to Elasticsearch 7. Index Indices, the largest unit of data in Elasticsearch, are logical partitions of documents and can be compared to a database in the world of relational databases.
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