Before going further into the book, we would like to emphasize that we are treating this book as an extension to the Elasticsearch Server Second Edition book we've written, also published by Packt Publishing. Of course, we start with a brief introduction to both Apache Lucene and Elasticsearch, but this book is not for a person who doesn't know Elasticsearch at all. We treat Mastering Elasticsearch as a book that will systematize your knowledge about Elasticsearch and extend it by showing some examples of how to leverage your knowledge in certain situations. If you are looking for a book that will help you start your journey into the world of Elasticsearch, please take a look at Elasticsearch Server Second Edition mentioned previously.
That said, we hope that by reading this book, you want to extend and build on basic Elasticsearch knowledge. We assume that you already know how to index data to Elasticsearch using single requests as well as bulk indexing. You should also know how to send queries to get the documents you are interested in, how to narrow down the results of your queries by using filtering, and how to calculate statistics for your data with the use of the faceting/aggregation mechanism. However, before getting to the exciting functionality that Elasticsearch offers, we think we should start with a quick tour of Apache Lucene, which is a full text search library that Elasticsearch uses to build and search its indices, as well as the basic concepts on which Elasticsearch is built. In order to move forward and extend our learning, we need to ensure that we don't forget the basics. This is easy to do. We also need to make sure that we understand Lucene correctly as Mastering Elasticsearch requires this understanding. By the end of this chapter, we will have covered the following topics:
What Apache Lucene is
What overall Lucene architecture looks like
How the analysis process is done
What Apache Lucene query language is and how to use it
What are the basic concepts of Elasticsearch
How Elasticsearch communicates internally
In order to fully understand how Elasticsearch works, especially when it comes to indexing and query processing, it is crucial to understand how Apache Lucene library works. Under the hood, Elasticsearch uses Lucene to handle document indexing. The same library is also used to perform a search against the indexed documents. In the next few pages, we will try to show you the basics of Apache Lucene, just in case you've never used it.
You may wonder why Elasticsearch creators decided to use Apache Lucene instead of developing their own functionality. We don't know for sure since we were not the ones who made the decision, but we assume that it was because Lucene is mature, open-source, highly performing, scalable, light and, yet, very powerful. It also has a very strong community that supports it. Its core comes as a single file of Java library with no dependencies, and allows you to index documents and search them with its out-of-the-box full text search capabilities. Of course, there are extensions to Apache Lucene that allow different language handling, and enable spellchecking, highlighting, and much more, but if you don't need those features, you can download a single file and use it in your application.
Although I would like to jump straight to Apache Lucene architecture, there are some things we need to know first in order to fully understand it, and those are as follows:
Document: It is a main data carrier used during indexing and search, containing one or more fields, that contain the data we put and get from Lucene.
Field: It is a section of the document which is built of two parts: the name and the value.
Term: It is a unit of search representing a word from the text.
Token: It is an occurrence of a term from the text of the field. It consists of term text, start and end offset, and a type.
Apache Lucene writes all the information to the structure called inverted index. It is a data structure that maps the terms in the index to the documents, not the other way round like the relational database does. You can think of an inverted index as a data structure, where data is term oriented rather than document oriented.
Let's see how a simple inverted index can look. For example, let's assume that we have the documents with only title field to be indexed and they look like the following:
Elasticsearch Server (document 1)
Mastering Elasticsearch (document 2)
Apache Solr 4 Cookbook (document 3)
So, the index (in a very simple way) could be visualized as shown in the following figure:
As you can see, each term points to the number of documents it is present in. This allows for a very efficient and fast search such as the term-based queries. In addition to this, each term has a number connected to it: the count, telling Lucene how often it occurs.
Each index is divided into multiple write once and read many time segments. When indexing, after a single segment is written to disk, it can't be updated. For example, the information about deleted documents is stored in a separate file, but the segment itself is not updated.
However, multiple segments can be merged together in a process called segments merge. After forcing, segments are merged, or after Lucene decides it is time for merging to be performed, segments are merged together by Lucene to create larger ones. This can be I/O demanding; however, it is needed to clean up some information because during that time some information that is not needed anymore is deleted, for example the deleted documents. In addition to this, searching with the use of one larger segment is faster than searching against multiple smaller ones holding the same data. However, once again, remember that segments merging is an I/O demanding operation and you shouldn't force merging, just configure your merge policy carefully.
Note
If you want to know what files are building the segments and what information is stored inside them, please take a look at Apache Lucene documentation available at http://lucene.apache.org/core/4_10_3/core/org/apache/lucene/codecs/lucene410/package-summary.html.
Of course, the actual index created by Lucene is much more complicated and advanced, and consists of more than the terms their counts and documents in which they are present. We would like to tell you about a few of those additional index pieces because even though they are internal, it is usually good to know about them as they can be very handy.
A norm is a factor associated with each indexed document and stores normalization factors used to compute the score relative to the query. Norms are computed on the basis of index time boosts and are indexed along with the documents. With the use of norms, Lucene is able to provide an index time-boosting functionality at the cost of a certain amount of additional space needed for norms indexation and some amount of additional memory.
Term vectors are small inverted indices per document. They consist of pairs—a term and its frequency—and can optionally include information about term position. By default, Lucene and Elasticsearch don't enable term vectors indexing, but some functionality such as the fast vector highlighting requires them to be present.
With the release of Lucene 4.0, the library introduced the so-called codec architecture, giving developers control over how the index files are written onto the disk. One of the parts of the index is the posting format, which stores fields, terms, documents, terms positions and offsets, and, finally, the payloads (a byte array stored at an arbitrary position in Lucene index, which can contain any information we want). Lucene contains different posting formats for different purposes, for example one that is optimized for high cardinality fields like the unique identifier.
As we already mentioned, Lucene index is the so-called inverted index. However, for certain features, such as faceting or aggregations, such architecture is not the best one. The mentioned functionality operates on the document level and not the term level and because Elasticsearch needs to uninvert the index before calculations can be done. Because of that, doc values were introduced and additional structure used for faceting, sorting and aggregations. The doc values store uninverted data for a field they are turned on for. Both Lucene and Elasticsearch allow us to configure the implementation used to store them, giving us the possibility of memory-based doc values, disk-based doc values, and a combination of the two.
Of course, the question arises of how the data passed in the documents is transformed into the inverted index and how the query text is changed into terms to allow searching. The process of transforming this data is called analysis.
Analysis is done by the analyzer, which is built of tokenizer and zero or more filters, and can also have zero or more character mappers.
A tokenizer in Lucene is used to divide the text into tokens, which are basically terms with additional information, such as its position in the original text and its length. The result of the tokenizer work is a so-called token stream, where the tokens are put one by one and are ready to be processed by filters.
Apart from tokenizer, Lucene analyzer is built of zero or more filters that are used to process tokens in the token stream. For example, it can remove tokens from the stream, change them or even produce new ones. There are numerous filters and you can easily create new ones. Some examples of filters are as follows:
ASCII folding filter: It removes non ASCII parts from tokens
Synonyms filter: It is responsible for changing one token to another on the basis of synonym rules
Multiple language stemming filters: These are responsible for reducing tokens (actually the text part that they provide) into their root or base forms, the stem
Filters are processed one after another, so we have almost unlimited analysis possibilities with adding multiple filters one after another.
The last thing is the character mappings, which is used before tokenizer and is responsible for processing text before any analysis is done. One of the examples of character mapper is the HTML tags removal process.
We may wonder how that all affects indexing and querying when using Lucene and all the software that is built on top of it. During indexing, Lucene will use an analyzer of your choice to process the contents of your document; different analyzers can be used for different fields, so the title field of your document can be analyzed differently compared to the description field.
During query time, if you use one of the provided query parsers, your query will be analyzed. However, you can also choose the other path and not analyze your queries. This is crucial to remember because some of the Elasticsearch queries are being analyzed and some are not. For example, the prefix query is not analyzed and the match query is analyzed.
What you should remember about indexing and querying analysis is that the index should be matched by the query term. If they don't match, Lucene won't return the desired documents. For example, if you are using stemming and lowercasing during indexing, you need to be sure that the terms in the query are also lowercased and stemmed, or your queries will return no results at all.
Some of the query types provided by Elasticsearch support Apache Lucene query parser syntax. Because of this, it is crucial to understand the Lucene query language.
A query is divided by Apache Lucene into terms and operators. A term, in Lucene, can be a single word or a phrase (group of words surrounded by double quote characters). If the query is set to be analyzed, the defined analyzer will be used on each of the terms that form the query.
A query can also contain Boolean operators that connect terms to each other forming clauses. The list of Boolean operators is as follows:
AND: It means that the given two terms (left and right operand) need to match in order for the clause to be matched. For example, we would run a query, such as apache
ANDlucene, to match documents with both apache and lucene terms in a document field.OR: It means that any of the given terms may match in order for the clause to be matched. For example, we would run a query, such as apache
ORlucene, to match documents with apache or lucene (or both) terms in a document field.NOT: It means that in order for the document to be considered a match, the term appearing after the
NOToperator must not match. For example, we would run a query luceneNOTElasticsearch to match documents that contain lucene term, but not the Elasticsearch term in the document field.
In addition to these, we may use the following operators:
+: It means that the given term needs to be matched in order for the document to be considered as a match. For example, in order to find documents that match lucene term and may match apache term, we would run a query such as +lucene apache.
-: It means that the given term can't be matched in order for the document to be considered a match. For example, in order to find a document with lucene term, but not Elasticsearch term, we would run a query such as +lucene -Elasticsearch.
When not specifying any of the previous operators, the default OR operator will be used.
In addition to all these, there is one more thing: you can use parenthesis to group clauses together for example, with something like the following query:
ElasticsearchAND(masteringORbook)
Of course, just like in Elasticsearch, in Lucene all your data is stored in fields that build the document. In order to run a query against a field, you need to provide the field name, add the colon character, and provide the clause that should be run against that field. For example, if you would like to match documents with the term Elasticsearch in the title field, you would run the following query:
title:Elasticsearch
You can also group multiple clauses. For example, if you would like your query to match all the documents having the Elasticsearch term and the mastering book phrase in the title field, you could run a query like the following code:
title:(+Elasticsearch +"mastering book")
The previous query can also be expressed in the following way:
+title:Elasticsearch +title:"mastering book"
In addition to the standard field query with a simple term or clause, Lucene allows us to modify the terms we pass in the query with modifiers. The most common modifiers, which you will be familiar with, are wildcards. There are two wildcards supported by Lucene, the ? and * terms. The first one will match any character and the second one will match multiple characters.
Note
Please note that by default these wildcard characters can't be used as the first character in a term because of performance reasons.
In addition to this, Lucene supports fuzzy and proximity searches with the use of the ~ character and an integer following it. When used with a single word term, it means that we want to search for terms that are similar to the one we've modified (the so-called fuzzy search). The integer after the ~ character specifies the maximum number of edits that can be done to consider the term similar. For example, if we would run a query, such as writer~2, both the terms writer and writers would be considered a match.
When the ~ character is used on a phrase, the integer number we provide is telling Lucene how much distance between the words is acceptable. For example, let's take the following query:
title:"mastering Elasticsearch"
It would match the document with the title field containing mastering Elasticsearch, but not mastering book Elasticsearch. However, if we would run a query, such as title:"mastering Elasticsearch"~2, it would result in both example documents matched.
We can also use boosting to increase our term importance by using the ^ character and providing a float number. Boosts lower than one would result in decreasing the document importance. Boosts higher than one will result in increasing the importance. The default boost value is 1. Please refer to the Default Apache Lucene scoring explained section in Chapter 2, Power User Query DSL, for further information on what boosting is and how it is taken into consideration during document scoring.
In addition to all these, we can use square and curly brackets to allow range searching. For example, if we would like to run a range search on a numeric field, we could run the following query:
price:[10.00 TO 15.00]
The preceding query would result in all documents with the price field between 10.00 and 15.00 inclusive.
In case of string-based fields, we also can run a range query, for example name:[Adam TO Adria].
The preceding query would result in all documents containing all the terms between Adam and Adria in the name field including them.
If you would like your range bound or bounds to be exclusive, use curly brackets instead of the square ones. For example, in order to find documents with the price field between 10.00 inclusive and 15.00 exclusive, we would run the following query:
price:[10.00 TO 15.00}
If you would like your range bound from one side and not bound by the other, for example querying for documents with a price higher than 10.00, we would run the following query:
price:[10.00 TO *]
Although we've said that we expect the reader to be familiar with Elasticsearch, we would really like you to fully understand Elasticsearch; therefore, we've decided to include a short introduction to the concepts of this great search engine.
As you probably know, Elasticsearch is production-ready software to build search and analysis-oriented applications. It was originally started by Shay Banon and published in February 2010. Since then, it has rapidly gained popularity just within a few years and has become an important alternative to other open source and commercial solutions. It is one of the most downloaded open source projects.
There are a few concepts that come with Elasticsearch and their understanding is crucial to fully understand how Elasticsearch works and operates.
Elasticsearch stores its data in one or more indices. Using analogies from the SQL world, index is something similar to a database. It is used to store the documents and read them from it. As already mentioned, under the hood, Elasticsearch uses Apache Lucene library to write and read the data from the index. What you should remember is that a single Elasticsearch index may be built of more than a single Apache Lucene index—by using shards.
Document is the main entity in the Elasticsearch world (and also in the Lucene world). At the end, all use cases of using Elasticsearch can be brought at a point where it is all about searching for documents and analyzing them. Document consists of fields, and each field is identified by its name and can contain one or multiple values. Each document may have a different set of fields; there is no schema or imposed structure—this is because Elasticsearch documents are in fact Lucene ones. From the client point of view, Elasticsearch document is a JSON object (see more on the JSON format at http://en.wikipedia.org/wiki/JSON).
Each document in Elasticsearch has its type defined. This allows us to store various document types in one index and have different mappings for different document types. If you would like to compare it to an SQL world, a type in Elasticsearch is something similar to a database table.
As already mentioned in the Introducing Apache Lucene section, all documents are analyzed before being indexed. We can configure how the input text is divided into tokens, which tokens should be filtered out, or what additional processing, such as removing HTML tags, is needed. This is where mapping comes into play—it holds all the information about the analysis chain. Besides the fact that Elasticsearch can automatically discover field type by looking at its value, in most cases we will want to configure the mappings ourselves to avoid unpleasant surprises.
The single instance of the Elasticsearch server is called a node. A single node in Elasticsearch deployment can be sufficient for many simple use cases, but when you have to think about fault tolerance or you have lots of data that cannot fit in a single server, you should think about multi-node Elasticsearch cluster.
Elasticsearch nodes can serve different purposes. Of course, Elasticsearch is designed to index and search our data, so the first type of node is the data node. Such nodes hold the data and search on them. The second type of node is the master node—a node that works as a supervisor of the cluster controlling other nodes' work. The third node type is the tribe node, which is new and was introduced in Elasticsearch 1.0. The tribe node can join multiple clusters and thus act as a bridge between them, allowing us to execute almost all Elasticsearch functionalities on multiple clusters just like we would be using a single cluster.
Cluster is a set of Elasticsearch nodes that work together. The distributed nature of Elasticsearch allows us to easily handle data that is too large for a single node to handle (both in terms of handling queries and documents). By using multi-node clusters, we can also achieve uninterrupted work of our application, even if several machines (nodes) are not available due to outage or administration tasks such as upgrade. Elasticsearch provides clustering almost seamlessly. In our opinion, this is one of the major advantages over competition; setting up a cluster in the Elasticsearch world is really easy.
As we said previously, clustering allows us to store information volumes that exceed abilities of a single server (but it is not the only need for clustering). To achieve this requirement, Elasticsearch spreads data to several physical Lucene indices. Those Lucene indices are called shards, and the process of dividing the index is called sharding. Elasticsearch can do this automatically and all the parts of the index (shards) are visible to the user as one big index. Note that besides this automation, it is crucial to tune this mechanism for particular use cases because the number of shard index is built or configured during index creation and cannot be changed without creating a new index and re-indexing the whole data.
Sharding allows us to push more data into Elasticsearch that is possible for a single node to handle. Replicas can help us in situations where the load increases and a single node is not able to handle all the requests. The idea is simple—create an additional copy of a shard, which can be used for queries just as original, primary shard. Note that we get safety for free. If the server with the primary shard is gone, Elasticsearch will take one of the available replicas of that shard and promote it to the leader, so the service work is not interrupted. Replicas can be added and removed at any time, so you can adjust their numbers when needed. Of course, the content of the replica is updated in real time and is done automatically by Elasticsearch.
Elasticsearch was built with a few concepts in mind. The development team wanted to make it easy to use and highly scalable. These core features are visible in every corner of Elasticsearch. From the architectural perspective, the main features are as follows:
Reasonable default values that allow users to start using Elasticsearch just after installing it, without any additional tuning. This includes built-in discovery (for example, field types) and auto-configuration.
Working in distributed mode by default. Nodes assume that they are or will be a part of the cluster.
Peer-to-peer architecture without single point of failure (SPOF). Nodes automatically connect to other machines in the cluster for data interchange and mutual monitoring. This covers automatic replication of shards.
Easily scalable both in terms of capacity and the amount of data by adding new nodes to the cluster.
Elasticsearch does not impose restrictions on data organization in the index. This allows users to adjust to the existing data model. As we noted in type description, Elasticsearch supports multiple data types in a single index, and adjustment to the business model includes handling relationships between documents (although, this functionality is rather limited).
Near Real Time (NRT) searching and versioning. Because of the distributed nature of Elasticsearch, it is impossible to avoid delays and temporary differences between data located on the different nodes. Elasticsearch tries to reduce these issues and provide additional mechanisms as versioning.
The following section will include information on key Elasticsearch features, such as bootstrap, failure detection, data indexing, querying, and so on.
When Elasticsearch node starts, it uses the discovery module to find the other nodes in the same cluster (the key here is the cluster name defined in the configuration) and connect to them. By default the multicast request is broadcast to the network to find other Elasticsearch nodes with the same cluster name. You can see the process illustrated in the following figure:
In the preceding figure, the cluster, one of the nodes that is master eligible is elected as master node (by default all nodes are master eligible). This node is responsible for managing the cluster state and the process of assigning shards to nodes in reaction to changes in cluster topology.
Note
Note that a master node in Elasticsearch has no importance from the user perspective, which is different from other systems available (such as the databases). In practice, you do not need to know which node is a master node; all operations can be sent to any node, and internally Elasticsearch will do all the magic. If necessary, any node can send sub-queries in parallel to other nodes and merge responses to return the full response to the user. All of this is done without accessing the master node (nodes operates in peer-to-peer architecture).
The master node reads the cluster state and, if necessary, goes into the recovery process. During this state, it checks which shards are available and decides which shards will be the primary shards. After this, the whole cluster enters into a yellow state.
This means that a cluster is able to run queries, but full throughput and all possibilities are not achieved yet (it basically means that all primary shards are allocated, but not all replicas are). The next thing to do is to find duplicated shards and treat them as replicas. When a shard has too few replicas, the master node decides where to put missing shards and additional replicas are created based on a primary shard (if possible). If everything goes well, the cluster enters into a green state (which means that all primary shards and all their replicas are allocated).
During normal cluster work, the master node monitors all the available nodes and checks whether they are working. If any of them are not available for the configured amount of time, the node is treated as broken and the process of handling failure starts. For example, this may mean rebalancing of shards, choosing new leaders, and so on. As another example, for every primary shard that is present on the failed nodes, a new primary shard should be elected from the remaining replicas of this shard. The whole process of placing new shards and replicas can (and usually should) be configured to match our needs. More information about it can be found in Chapter 7, Elasticsearch Administration.
Just to illustrate how it works, let's take an example of a three nodes cluster. One of the nodes is the master node, and all of the nodes can hold data. The master node will send the ping requests to other nodes and wait for the response. If the response doesn't come (actually how many ping requests may fail depends on the configuration), such a node will be removed from the cluster. The same goes in the opposite way—each node will ping the master node to see whether it is working.
We talked about how Elasticsearch is built, but, after all, the most important part for us is how to feed it with data and how to build queries. In order to do that, Elasticsearch exposes a sophisticated Application Program Interface (API). In general, it wouldn't be a surprise if we would say that every feature of Elasticsearch has an API. The primary API is REST based (see http://en.wikipedia.org/wiki/Representational_state_transfer) and is easy to integrate with practically any system that can send HTTP requests.
Elasticsearch assumes that data is sent in the URL or in the request body as a JSON document (see http://en.wikipedia.org/wiki/JSON). If you use Java or language based on Java Virtual Machine (JVM), you should look at the Java API, which, in addition to everything that is offered by the REST API, has built-in cluster discovery. It is worth mentioning that the Java API is also internally used by Elasticsearch itself to do all the node-to-node communication. Because of this, the Java API exposes all the features available through the REST API calls.
There are a few ways to send data to Elasticsearch. The easiest way is using the index API, which allows sending a single document to a particular index. For example, by using the
curl tool (see http://curl.haxx.se/). An example command that would create a new document would look as follows:
curl -XPUT http://localhost:9200/blog/article/1 -d '{"title": "New version of Elastic Search released!", "tags": ["announce", "Elasticsearch", "release"] }'
The second way allows us to send many documents using the bulk API and the UDP bulk API. The difference between these methods is the connection type. Common bulk command sends documents by HTTP protocol and UDP bulk sends this using connection less datagram protocol. This is faster but not so reliable. The last method uses plugins, called rivers, but let's not discuss them as the rivers will be removed in future versions of Elasticsearch.
One very important thing to remember is that the indexing will always be first executed at the primary shard, not on the replica. If the indexing request is sent to a node that doesn't have the correct shard or contains a replica, it will be forwarded to the primary shard. Then, the leader will send the indexing request to all the replicas, wait for their acknowledgement (this can be controlled), and finalize the indexation if the requirements were met (like the replica quorum being updated).
The following illustration shows the process we just discussed:
The Query API is a big part of Elasticsearch API. Using the Query DSL (JSON-based language for building complex queries), we can do the following:
Use various query types including simple term query, phrase, range, Boolean, fuzzy, span, wildcard, spatial, and function queries for human readable scoring control
Build complex queries by combining the simple queries together
Filter documents, throwing away ones that do not match selected criteria without influencing the scoring, which is very efficient when it comes to performance
Find documents similar to a given document
Find suggestions and corrections of a given phrase
Build dynamic navigation and calculate statistics using aggregations
Use prospective search and find queries matching a given document
When talking about querying, the important thing is that query is not a simple, single-stage process. In general, the process can be divided into two phases: the scatter phase and the gather phase. The scatter phase is about querying all the relevant shards of your index. The gather phase is about gathering the results from the relevant shards, combining them, sorting, processing, and returning to the client. The following illustration shows that process:
As we said in the beginning of this chapter, we treat the book you are holding in your hands as a continuation of the Elasticsearch Server Second Edition book. Because of this, we would like to continue the story that we've used in that book. In general, we assume that we are implementing and running an online book store, as simple as that.
The mappings for our library index look like the following:
{
"book" : {
"_index" : {
"enabled" : true
},
"_id" : {
"index": "not_analyzed",
"store" : "yes"
},
"properties" : {
"author" : {
"type" : "string"
},
"characters" : {
"type" : "string"
},
"copies" : {
"type" : "long",
"ignore_malformed" : false
},
"otitle" : {
"type" : "string"
},
"tags" : {
"type" : "string",
"index" : "not_analyzed"
},
"title" : {
"type" : "string"
},
"year" : {
"type" : "long",
"ignore_malformed" : false
},
"available" : {
"type" : "boolean"
},
"review" : {
"type" : "nested",
"properties" : {
"nickname" : {
"type" : "string"
},
"text" : {
"type" : "string"
},
"stars" : {
"type" : "integer"
}
}
}
}
}
}The mappings can be found in the library.json file provided with the book.
The data that we will use is provided with the book in the books.json file. The example documents from that file look like the following:
{ "index": {"_index": "library", "_type": "book", "_id": "1"}}
{ "title": "All Quiet on the Western Front","otitle": "Im Westen nichts
Neues","author": "Erich Maria Remarque","year": 1929,"characters": ["Paul
Bäumer", "Albert Kropp", "Haie Westhus", "Fredrich Müller", "Stanislaus
Katczinsky", "Tjaden"],"tags": ["novel"],"copies": 1, "available": true,
"section" : 3}
{ "index": {"_index": "library", "_type": "book", "_id": "2"}}
{ "title": "Catch-22","author": "Joseph Heller","year": 1961,"characters":
["John Yossarian", "Captain Aardvark", "Chaplain Tappman", "Colonel
Cathcart", "Doctor Daneeka"],"tags": ["novel"],"copies": 6, "available" :
false, "section" : 1}
{ "index": {"_index": "library", "_type": "book", "_id": "3"}}
{ "title": "The Complete Sherlock Holmes","author": "Arthur Conan
Doyle","year": 1936,"characters": ["Sherlock Holmes","Dr. Watson", "G.
Lestrade"],"tags": [],"copies": 0, "available" : false, "section" : 12}
{ "index": {"_index": "library", "_type": "book", "_id": "4"}}
{ "title": "Crime and Punishment","otitle": "Преступлéние и
наказáние","author": "Fyodor Dostoevsky","year": 1886,"characters":
["Raskolnikov", "Sofia Semyonovna Marmeladova"],"tags": [],"copies": 0,
"available" : true}Tip
Downloading the example code
You can download the example code files for all Packt books you have purchased from your account at http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.
To create the index using the provided mappings and to index the data, we would run the following commands:
curl -XPOST 'localhost:9200/library' curl -XPUT 'localhost:9200/library/book/_mapping' -d @library.json curl -s -XPOST 'localhost:9200/_bulk' --data-binary @books.json
In this chapter, we looked at the general architecture of Apache Lucene: how it works, how the analysis process is done, and how to use Apache Lucene query language. In addition to that, we discussed the basic concepts of Elasticsearch, its architecture, and internal communication.
In the next chapter, you'll learn about the default scoring formula Apache Lucene uses, what the query rewrite process is, and how it works. In addition to that, we'll discuss some of the Elasticsearch functionality, such as query templates, filters, and how they affect performance, what we can do with that, and how we can choose the right query to get the job done.
