ElasticSearch

Elasticsearch Connector as Source in Flink

由 馋奶兔 提交于 2021-01-07 04:15:33
问题 I used Elasticsearch Connector as a Sink to insert data into Elasticsearch (see : https://ci.apache.org/projects/flink/flink-docs-release-1.7/dev/connectors/elasticsearch.html). But, I did not found any connector to get data from Elasticsearch as source. Is there any connector or example to use Elasticsearch documents as source in a Flink pipline? Regards, Ali 回答1: I finaly defined a simple read from ElasticSearch function public static class ElasticsearchFunction extends ProcessFunction

Elasticsearch Connector as Source in Flink

由 只谈情不闲聊 提交于 2021-01-07 04:15:09
问题 I used Elasticsearch Connector as a Sink to insert data into Elasticsearch (see : https://ci.apache.org/projects/flink/flink-docs-release-1.7/dev/connectors/elasticsearch.html). But, I did not found any connector to get data from Elasticsearch as source. Is there any connector or example to use Elasticsearch documents as source in a Flink pipline? Regards, Ali 回答1: I finaly defined a simple read from ElasticSearch function public static class ElasticsearchFunction extends ProcessFunction

ELK. Nested values are not found

由 ￣綄美尐妖づ 提交于 2021-01-07 04:14:09
问题 I have index mapping like below: { "mapping": { "properties": { "MyMapProperty": { "type": "nested", "properties": { "first": { "type": "text", "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } }, "second": { "type": "text", "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } }, } }, "SecondProperty": { "type": "text", "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } }, "ThirdProperty": { "type": "text", "fields": { "keyword": { "type":

How to get kibana scripted fields through es2csv 5.x?

由 旧巷老猫 提交于 2021-01-07 02:50:23
问题 I have some kibana scripted fields that I want to extract into csv. I am using es2csv 5.x version. What I am trying es2csv -u localhost:9200 -i index_name -f kibana_scripted_fields -o output.csv But it's not giving me the output. Is there any way to achieve that ?? Thanks In Advance !!! 来源： https://stackoverflow.com/questions/64981299/how-to-get-kibana-scripted-fields-through-es2csv-5-x

How to make Filter Aggregations using NEST?

由 試著忘記壹切 提交于 2021-01-07 01:29:51
问题 I have a requirement for filter aggregations using NEST. But since I don't know much about this hence, I have made the below class Program { static void Main(string[] args) { ISearchResponse<TestReportModel> searchResponse = ConnectionToES.EsClient() .Search<TestReportModel> (s => s .Index("feedbackdata") .From(0) .Size(50000) .Query(q =>q.MatchAll()) ); var testRecords = searchResponse.Documents.ToList<TestReportModel>(); result = ComputeTrailGap(testRecords); } private static List<TestModel

How to make Filter Aggregations using NEST?

由 风流意气都作罢 提交于 2021-01-07 01:25:36
问题 I have a requirement for filter aggregations using NEST. But since I don't know much about this hence, I have made the below class Program { static void Main(string[] args) { ISearchResponse<TestReportModel> searchResponse = ConnectionToES.EsClient() .Search<TestReportModel> (s => s .Index("feedbackdata") .From(0) .Size(50000) .Query(q =>q.MatchAll()) ); var testRecords = searchResponse.Documents.ToList<TestReportModel>(); result = ComputeTrailGap(testRecords); } private static List<TestModel

Sending Data From Elasticsearch to AWS Databases in Real Time

由 只谈情不闲聊 提交于 2021-01-07 01:19:08
问题 I know this is a very different use case for Elasticsearch and I need your help. Main structure (can't be changed): There are some physical machines and we have sensors there. Data from these sensors are going to AWS Greengrass. Then, with Lambda function data are going to Elasticsearch by using MQTT. Elasticsearch is running on the docker. This is the structure and until here everything is ready and running ✅ Now, on the top of the ES I need some software that can send this data by using

Sending Data From Elasticsearch to AWS Databases in Real Time

由 僤鯓⒐⒋嵵緔 提交于 2021-01-07 01:03:32
问题 I know this is a very different use case for Elasticsearch and I need your help. Main structure (can't be changed): There are some physical machines and we have sensors there. Data from these sensors are going to AWS Greengrass. Then, with Lambda function data are going to Elasticsearch by using MQTT. Elasticsearch is running on the docker. This is the structure and until here everything is ready and running ✅ Now, on the top of the ES I need some software that can send this data by using

SkyWalking 分布式追踪系统

由 旧时模样 提交于 2021-01-06 23:08:41
随着微服务架构的流行，一些微服务架构下的问题也会越来越突出，比如一个请求会涉及多个服务，而服务本身可能也会依赖其他服务，整个请求路径就构成了一个网状的调用链，而在整个调用链中一旦某个节点发生异常，整个调用链的稳定性就会受到影响，所以会深深的感受到 “银弹” 这个词是不存在的，每种架构都有其优缺点 。 service map 面对以上情况， 我们就需要一些可以帮助理解系统行为、用于分析性能问题的工具，以便发生故障的时候，能够快速定位和解决问题，这时候 APM（应用性能管理）工具就该闪亮登场了。 目前主要的一些 APM 工具有: Cat、Zipkin、Pinpoint、SkyWalking，这里主要介绍 SkyWalking ，它是一款优秀的国产 APM 工具，包括了分布式追踪、性能指标分析、应用和服务依赖分析等。 下面是 SkyWalking 6.x 的架构图： 6.x architecture 说明： SkyWalking 的核心是数据分析和度量结果的存储平台，通过 HTTP 或 gRPC 方式向 SkyWalking Collecter 提交分析和度量数据，SkyWalking Collecter 对数据进行分析和聚合，存储到 Elasticsearch、H2、MySQL、TiDB 等其一即可，最后我们可以通过 SkyWalking UI 的可视化界面对最终的结果进行查看

SpringBoot微服务的监控与运维

由 自闭症网瘾萝莉.ら 提交于 2021-01-06 14:33:29
与大部分应用和系统一样， SpringBoot 微服务的开发、发布与部署只占其生命周期的一小部分，应用和系统运维才是重中之重。而运维过程中，监控工作更是占据重要位置。 运维的目的之一是为了保证系统的平稳运行，进而保障公司业务能持续对外服务，为了达到这一目的，我们需要对系统的状态进行持续地观测，以期望一有风吹草动就能发现并作出应对，监控作为一种手段，就是以此为生。 我们会从以下多个层面对 Spring Boot 微服务进行监控： 硬件层面 网络层面 系统层面 SpringBoot 微服务的应用层面 服务访问层面 我们会从所有这些层面采集相应的状态数据，然后汇总，存储，并分析，一旦某项指标超出规定的阈值，则报警，在接收到报警通知之后，我们需要做出应对以改变现在系统状态不健康的局面，这一般通过预置的调控开关来调整应用状态，要么重启或者服务降级，也就是执行监控的“控”，整个过程如图 1 所示。 硬件、网络以及系统层面的监控，现有的一些监控系统和方案已经可以很好地提供支持，比如开源的 Zabbix 系统或者以报警为强项的 Nagios 系统。 本节不对这些层面的监控做过多介绍，我们将更多对 SpringBoot 微服务应用层面的监控进行实践方案的探索。SpringBoot 微服务的内部状态，通过多种方式或者渠道可以知道。 打印的应用日志是一种 SpringBoot 微服务运行状态的反映形式。
订阅 ElasticSearch