ElasticSearch

How to export pandas data to elasticsearch?

由 一笑奈何 提交于 2020-12-29 09:36:47
问题 It is possible to export a pandas dataframe data to elasticsearch using elasticsearch-py . For example, here is some code: https://www.analyticsvidhya.com/blog/2017/05/beginners-guide-to-data-exploration-using-elastic-search-and-kibana/ There are a lot of similar methods like to_excel , to_csv , to_sql . Is there a to_elastic method? If no, where should I request it? 回答1: The following script works for localhost: import numpy as np import pandas as pd df = pd.DataFrame(np.random.randint(0,100

How to export pandas data to elasticsearch?

由 ⅰ亾dé卋堺 提交于 2020-12-29 09:36:26
问题 It is possible to export a pandas dataframe data to elasticsearch using elasticsearch-py . For example, here is some code: https://www.analyticsvidhya.com/blog/2017/05/beginners-guide-to-data-exploration-using-elastic-search-and-kibana/ There are a lot of similar methods like to_excel , to_csv , to_sql . Is there a to_elastic method? If no, where should I request it? 回答1: The following script works for localhost: import numpy as np import pandas as pd df = pd.DataFrame(np.random.randint(0,100

一个好习惯——每年至少更新一次简历

由 落爺英雄遲暮 提交于 2020-12-28 15:47:30
一个好习惯——每年至少更新一次自己的简历，而不是等到找工作才写简历。 1、简历是随时间、产品开发或项目经历积累出来的，不是现写的 项目结束或者产品发布后的当天晚上是最佳时机！！越往后，记忆曲线决定信息丢失率越高！ 一页 ppt 或者一页A4纸肯定就能介绍清楚，如果还不够，说明：还需要提炼。 如果：写不出来东西，你该反思了？？ 这个项目一点收获没有吗？ 架构能力、设计、开发、运维、管理提升的点都是可以写的点。 2、人的记忆周期是有限的，总结要写出来，单靠脑子不灵 哪怕你负责的项目、你带过的团队，三年、五年后，你可能会连项目名都不记得了。 别拖延，哪怕你有拖延症。 面试别人的时候，问到一个项目经历，经常收到的回复：这个项目比较久了，忘记了。 我一般会进一步思考：忘记了可以不写，但一旦写了，你就得能讲出来！！这很重要。 所谓：重点突出、详略得当也大抵不过如此！ 3、简历准备重点突出什么，你就积累什么 包含不限制： 第一：项目背景（概述）； 第二：使用技术； 第三：我负责内容（团队人数）和工作亮点（最多三个）。 4、写完后，要自己能复述出来 咱们程序员通病：写代码可以，和测试吵架可以。 但，你让我概括讲出来我做的项目？不行，不会讲。 能写出来已经很不错，但再能讲出来，就更厉害了。 现在不讲，面试就很容易支支吾吾，影响整体面试体验。 5、简历的确需要常写常新 找到靠谱的模板，至少每年更新一次

How to create request body for Python Elasticsearch mSearch

由 ぐ巨炮叔叔 提交于 2020-12-28 13:22:28
问题 I'm trying to run a multi search request on the Elasticsearch Python client. I can run the singular search correctly but can't figure out how to format the request for a msearch. According to the documentation, the body of the request needs to be formatted as: The request definitions (metadata-search request definition pairs), as either a newline separated string, or a sequence of dicts to serialize (one per row). What's the best way to create this request body? I've been searching for

Exceptionless 本地部署

由 淺唱寂寞╮ 提交于 2020-12-28 04:27:11
copy to:https://www.cnblogs.com/uptothesky/p/5864863.html 本地部署官方wiki .NET 4.6.1 这个因为我装了VS2015，就没有单独再装了 Java JDK 1.8+ 安装完后还需配置下Java环境，系统变量添加：JAVA_HOME 对应 C:\Program Files\Java\jdk1.8.0_102 是安装jdk的目录，用户变量Path 中添加 %JAVA_HOME%\bin; 配置完成后打开cmd，运行 java - version 如果报错的话有很多种可能，搜索一下会有解决方案，我的就是在C:\Windows\System32 目录下把java.exe改名成javaa.exe，再次cmd运行就成功了 IIS 8+ 这个感觉不是强制的，我win7的IIS 7.5也是可以的 ElasticSearch 1.7.5 ( Elasticsearch 2.x is not yet supported ) 到连接地址去下载1.7.5版本，人家已经说明2.x的版本不支持，找这个历史版本得翻好几页，大概在第7页左右，直接给个下载连接： elasticsearch-1.7.5 ，下载完后解压 下载最新的 latest Exceptionless release artifact ZIP ，下载后解压

How to query an Elasticsearch index using Pyspark and Dataframes

由 喜欢而已 提交于 2020-12-28 00:04:55
问题 Elasticsaerch's documentation only covers loading a complete index to Spark. from pyspark.sql import SQLContext sqlContext = SQLContext(sc) df = sqlContext.read.format("org.elasticsearch.spark.sql").load("index/type") df.printSchema() How can you perform a query to return data from an Elasticsearch index and load them to Spark as a DataFrame using pyspark? 回答1: Below is how I do it. General environment settings and command: export SPARK_HOME=/home/ezerkar/spark-1.6.0-bin-hadoop2.6 export

How to query an Elasticsearch index using Pyspark and Dataframes

由 六眼飞鱼酱① 提交于 2020-12-28 00:04:25
问题 Elasticsaerch's documentation only covers loading a complete index to Spark. from pyspark.sql import SQLContext sqlContext = SQLContext(sc) df = sqlContext.read.format("org.elasticsearch.spark.sql").load("index/type") df.printSchema() How can you perform a query to return data from an Elasticsearch index and load them to Spark as a DataFrame using pyspark? 回答1: Below is how I do it. General environment settings and command: export SPARK_HOME=/home/ezerkar/spark-1.6.0-bin-hadoop2.6 export

How to query an Elasticsearch index using Pyspark and Dataframes

由 倾然丶 夕夏残阳落幕 提交于 2020-12-28 00:03:20
问题 Elasticsaerch's documentation only covers loading a complete index to Spark. from pyspark.sql import SQLContext sqlContext = SQLContext(sc) df = sqlContext.read.format("org.elasticsearch.spark.sql").load("index/type") df.printSchema() How can you perform a query to return data from an Elasticsearch index and load them to Spark as a DataFrame using pyspark? 回答1: Below is how I do it. General environment settings and command: export SPARK_HOME=/home/ezerkar/spark-1.6.0-bin-hadoop2.6 export

ElasticSearch - cross_fields multi match with fuzzy search

由 二次信任 提交于 2020-12-27 17:04:25
问题 I have documents that represent users. They have fields name and surname . Let's say I have two users indexed - Michael Jackson and Michael Starr. I want these sample searches to work: Michael => { Michael Jackson , Michael Starr } Jack Mich => { Michael Jackson } (incomplete words and reversed order) Michal Star => { Michael Starr } (fuzzy search) I tried different queries and got the best results from multi_match query with cross_fields type. There are 2 problems though: It only finds

ElasticSearch - cross_fields multi match with fuzzy search

由 混江龙づ霸主 提交于 2020-12-27 17:04:12
问题 I have documents that represent users. They have fields name and surname . Let's say I have two users indexed - Michael Jackson and Michael Starr. I want these sample searches to work: Michael => { Michael Jackson , Michael Starr } Jack Mich => { Michael Jackson } (incomplete words and reversed order) Michal Star => { Michael Starr } (fuzzy search) I tried different queries and got the best results from multi_match query with cross_fields type. There are 2 problems though: It only finds
订阅 ElasticSearch