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Article directory
Aggregation and Analysis
Performing aggregation and measurement operations in Elasticsearch can help us perform deeper analysis on the data. This article describes how to use aggregations and measures to perform complex data analysis operations such as counts, averages, percentiles, and grouping.
perform aggregate operations
1. Use the Java API to perform aggregate operations
Various aggregate operations can be performed using the Java API. The following is a code example that uses a RestHighLevelClient object to perform an aggregation of terms in an index named my_index:
SearchRequest request = new SearchRequest("my_index");
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
TermsAggregationBuilder aggregation =
AggregationBuilders.terms("by_age").field("age");
sourceBuilder.aggregation(aggregation);
request.source(sourceBuilder);
SearchResponse response = client.search(request, RequestOptions.DEFAULT);
The above code uses the SearchRequest object and the SearchSourceBuilder object to perform the terms aggregation operation and group by the age field.
2. Use the CURL command to perform aggregation operations
Various aggregate operations can also be performed using the CURL command. The following is an example of retrieving all documents using the terms aggregation operation in an index named my_index:
curl -XGET 'localhost:9200/my_index/_search?pretty' -H 'Content-Type: application/json' -d'
{
"aggs" : {
"by_age" : {
"terms" : { "field" : "age" }
}
}
}
'
Execute measure operations
1. Use the Java API to perform measurement operations
Various metric operations can be performed using the Java API. The following is a code example that uses a RestHighLevelClient object to perform an avg metric operation in an index named my_index:
SearchRequest request = new SearchRequest("my_index");
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
AvgAggregationBuilder aggregation =
AggregationBuilders.avg("avg_age").field("age");
sourceBuilder.aggregation(aggregation);
request.source(sourceBuilder);
SearchResponse response = client.search(request, RequestOptions.DEFAULT);
The above code uses the SearchRequest object and the SearchSourceBuilder object to perform the avg measure operation and returns the average value of the age field.
2. Use the CURL command to perform measurement operations
Various metric operations can also be performed using the CURL command. The following is an example of retrieving all documents using the avg metric operation in an index named my_index:
curl -XGET 'localhost:9200/my_index/_search?pretty' -H 'Content-Type: application/json' -d'
{
"aggs" : {
"avg_age" : {
"avg" : { "field" : "age" }
}
}
}
'
in conclusion
This article describes how to use aggregations and measures to perform complex data analysis operations such as counts, averages, percentiles, and grouping. The data in the Elasticsearch index can be aggregated and measured using the Java API or CURL commands to better understand and analyze the data. In practical applications, it is necessary to select appropriate aggregation and measurement operations for use according to specific needs.
Search performance optimization
Optimizing Elasticsearch's search performance is a very important part of your application. This article describes how to optimize Elasticsearch search performance by using caching, adjusting the size and number of shards, and using search suggestions.
use cache
There are two types of caches in Elasticsearch: query cache and filter cache. Query caching provides fast responses to the same query results, while filter caching caches filter results for quick use in subsequent searches. Here is a code example to enable filter caching using the Java API:
SearchRequest request = new SearchRequest("my_index");
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
sourceBuilder.query(QueryBuilders.termQuery("age", 30));
sourceBuilder.postFilter(QueryBuilders.termQuery("city", "New York"));
sourceBuilder.size(0);
sourceBuilder.aggregation(AggregationBuilders.avg("avg_age").field("age"));
sourceBuilder.aggregation(AggregationBuilders.terms("by_city").field("city"));
sourceBuilder.profile(true);
sourceBuilder.cache(true);
request.source(sourceBuilder);
SearchResponse response = client.search(request, RequestOptions.DEFAULT);
The above code enables filter caching using the SearchSourceBuilder object.
Adjust the size and number of shards
A shard is the basic unit of data in Elasticsearch, and dividing data into multiple shards allows Elasticsearch to better handle large datasets. However, if the shard is too large or too small, it will affect the search performance. The following is a code example for setting the number of index shards and backups using the Java API:
CreateIndexRequest request = new CreateIndexRequest("my_index");
request.settings(Settings.builder()
.put("index.number_of_shards", 5)
.put("index.number_of_replicas", 1));
The above code uses the CreateIndexRequest object to set the number of shards of the index named my_index to 5 and the number of backups to 1.
Use search suggestions
Search suggestion is an important search optimization technique in Elasticsearch. It can provide features such as auto-completion, spell-checking, and relevance suggestions as users enter search queries. Here's a code sample for adding full text-based recommendation search suggestions using the Java API:
SearchRequest request = new SearchRequest("my_index");
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
SuggestionBuilder termSuggestionBuilder =
SuggestBuilders.termSuggestion("name").text("jonh");
SuggestBuilder suggestBuilder = new SuggestBuilder();
suggestBuilder.addSuggestion("suggest_name", termSuggestionBuilder);
sourceBuilder.suggest(suggestBuilder);
request.source(sourceBuilder);
SearchResponse response = client.search(request, RequestOptions.DEFAULT);
The above code adds a full text-based suggested search suggestion using the SearchSourceBuilder object.
in conclusion
This article describes how to optimize Elasticsearch search performance by using caching, adjusting the size and number of shards, and using search suggestions. Using these technologies can improve search responsiveness and enhance the user experience. In practical applications, it is necessary to select an appropriate optimization method according to specific search requirements.
cluster management
Configuring and managing an Elasticsearch cluster is key to making large-scale Elasticsearch applications run successfully. This article will introduce how to configure and manage Elasticsearch clusters by performing operations such as node discovery, load balancing, and failover.
node discovery
Node discovery is an important concept in Elasticsearch, which allows new nodes to join an existing Elasticsearch cluster. The following is a code example to enable node discovery using the Java API:
Settings settings = Settings.builder()
.put("discovery.seed_hosts", "host1:9300,host2:9300")
.put("cluster.name", "my_cluster_name")
.build();
TransportClient client = new PreBuiltTransportClient(settings);
The above code enables node discovery using the Settings object and sets the node list to host1 and host2.
load balancing
Load balancing is a very important part in a distributed system, it can ensure that all nodes in the system carry the load evenly. The following is a code example to add load balancing function using Java API:
RestHighLevelClient client = new RestHighLevelClient(
RestClient.builder(new HttpHost("localhost", 9200, "http"))
.setHttpClientConfigCallback(httpClientBuilder ->
httpClientBuilder.addInterceptorLast(new ElasticsearchInterceptor())));
The above code uses the RestClient object to add an interceptor named ElasticsearchInterceptor to achieve load balancing.
failover
Failover is an issue that must be considered in an Elasticsearch cluster. When a node fails, immediate action is required to replace it with another node. Here is a code example to add automatic failover functionality using the Java API:
Settings settings = Settings.builder()
.put("cluster.routing.allocation.enable", "all")
.put("cluster.routing.allocation.node_initial_primaries_recoveries", 20)
.put("cluster.routing.allocation.node_concurrent_recoveries", 2)
.put("indices.recovery.max_bytes_per_sec", "50mb")
.build();
The above code enables the automatic failover function and sets some related parameters, such as index recovery speed and number of concurrent recovery.
in conclusion
This article describes how to configure and manage an Elasticsearch cluster for node discovery, load balancing, and failover. These techniques can make Elasticsearch applications more stable, reliable and efficient. In practical applications, it is necessary to select appropriate configuration options and management solutions to meet specific needs.
Security and Access Control
Securing your Elasticsearch cluster and data is a must for any production application. This article describes how to improve the security of Elasticsearch using techniques such as access control, encryption, and authentication.
Access control
Access control is a very important concept in Elasticsearch, which ensures that only authorized users can access the Elasticsearch cluster and data. Here is a code sample for adding username/password based access control using the Java API:
RestHighLevelClient client = new RestHighLevelClient(
RestClient.builder(new HttpHost("localhost", 9200, "http"))
.setHttpClientConfigCallback(httpClientBuilder ->
httpClientBuilder.setDefaultCredentialsProvider(
new BasicCredentialsProvider()))
.setRequestConfigCallback(requestConfigBuilder ->
requestConfigBuilder.setConnectTimeout(5000)
.setSocketTimeout(60000)));
The above code uses the RestClient object to add a BasicCredentialsProvider object as the default credential provider to implement username/password based access control.
encryption
Encryption ensures security within the Elasticsearch cluster and during data transfer. The following is a code sample to enable HTTPS encryption using the Java API:
RestHighLevelClient client = new RestHighLevelClient(
RestClient.builder(new HttpHost("localhost", 9200, "https")));
The above code uses the RestClient object to enable the HTTPS encryption protocol to ensure the security of data transmission.
Authentication
Authentication is a very important concept in Elasticsearch, which ensures that only authorized users can access and modify Elasticsearch clusters and data. The following is a code sample for adding X-Pack based authentication functionality using the Java API:
RestHighLevelClient client = new RestHighLevelClient(
RestClient.builder(new HttpHost("localhost", 9200, "https"))
.setHttpClientConfigCallback(httpClientBuilder ->
httpClientBuilder.setDefaultCredentialsProvider(
new BasicCredentialsProvider()))
.setRequestConfigCallback(requestConfigBuilder ->
requestConfigBuilder.setConnectTimeout(5000)
.setSocketTimeout(60000))
.setXpackBuilder(XPackClientBuilder.builder("username", "password")));
The above code enables X-Pack based authentication using the RestClient object and sets the username and password to "username" and "password".
in conclusion
This article describes how to improve the security of Elasticsearch using techniques such as access control, encryption, and authentication. These technologies ensure the security of the Elasticsearch cluster and data and protect it from unauthorized access and attacks. In practical applications, appropriate security measures need to be selected according to specific requirements.
application integration
Integrating Elasticsearch into applications is key to enabling data search and analysis. This article will show you how to integrate Elasticsearch into your application using the REST API and various client libraries.
REST API
Elasticsearch provides a REST API so that applications can interact with Elasticsearch through the HTTP protocol. Here is an example of adding documents to an Elasticsearch index using Java code:
RestHighLevelClient client = new RestHighLevelClient(
RestClient.builder(new HttpHost("localhost", 9200, "http")));
IndexRequest request = new IndexRequest("my_index");
request.id("1");
String jsonString = "{" +
"\"name\":\"John\"," +
"\"age\":30," +
"\"city\":\"New York\"" +
"}";
request.source(jsonString, XContentType.JSON);
IndexResponse response = client.index(request, RequestOptions.DEFAULT);
The above code uses a RestHighLevelClient object to add a document with ID 1 to the index named "my_index".
client library
Elasticsearch also provides client libraries in various languages so that applications can interact with Elasticsearch more easily. The following is a code example to add the Elasticsearch client library using the Java API:
<dependency>
<groupId>org.elasticsearch.client</groupId>
<artifactId>elasticsearch-rest-high-level-client</artifactId>
<version>7.14.0</version>
</dependency>
The above code adds the elasticsearch-rest-high-level-client client library to the Java project.
Here is an example of a client library that uses Java code to add documents to an Elasticsearch index:
RestHighLevelClient client = new RestHighLevelClient(
RestClient.builder(new HttpHost("localhost", 9200, "http")));
IndexRequest request = new IndexRequest("my_index");
request.id("1");
Map<String, Object> jsonMap = new HashMap<>();
jsonMap.put("name", "John");
jsonMap.put("age", 30);
jsonMap.put("city", "New York");
request.source(jsonMap);
IndexResponse response = client.index(request, RequestOptions.DEFAULT);
The above code uses the RestHighLevelClient object and the Elasticsearch client library to add a document with ID 1 to the index named "my_index".
in conclusion
This article describes how to integrate Elasticsearch into applications using the REST API and client libraries in various languages. These methods enable applications to interact with Elasticsearch more efficiently and enable functions such as data search and analysis. In practical applications, it is necessary to select an appropriate integration method according to specific needs.