Hive

实战kudu集成impala

由 大憨熊 提交于 2021-02-20 05:51:48
推荐阅读： 论主数据的重要性（正确理解元数据、数据元） CDC+ETL实现数据集成方案 Java实现impala操作kudu 实战kudu集成impala impala基本介绍 ​ impala是基于hive的大数据分析查询引擎，直接使用hive的元数据库metadata，意味着impala元数据都存储在hive的metastore当中，并且impala兼容hive的绝大多数sql语法。所以需要安装impala的话，必须先安装hive，保证hive安装成功，并且还需要启动hive的metastore服务 　　impala是cloudera提供的一款高效率的sql查询工具，提供实时的查询效果，官方测试性能比hive快10到100倍，其sql查询比sparkSQL还要更加快速，号称是当前大数据领域最快的查询sql工具， 　　impala是参照谷歌的新三篇论文（Caffeine--网络搜索引擎、Pregel--分布式图计算、Dremel--交互式分析工具）当中的Dremel实现而来，其中旧三篇论文分别是（BigTable，GFS，MapReduce）分别对应我们即将学的HBase和已经学过的HDFS以及MapReduce。 　　impala是基于hive并使用内存进行计算，兼顾数据仓库，具有实时，批处理，多并发等优点 　　Kudu与Apache Impala （孵化）紧密集成

impala-kudu

由 末鹿安然 提交于 2021-02-20 05:38:02
kkudu 提供了自己的api来对kudu进行操作，但是有的开发人员习惯用jdbc来操作数据库，这里我们采用impala 来实现该功能， impala 安装积配置，请百度，我们的集群采用kerberos认证。 1.jdbc:impala 连接（impala 官方建议连接方式） 官网 下载cloudera-connector zip zip 中的其他jar包我们的集群已经有了，所以只加了ImpalaJDBC41.jar依赖 提交代码所在用户的 principal 是：impala/host@EXAMPLE.COM 连接代码如下： import java.sql.DriverManager val driverName = " com.cloudera.impala.jdbc41.Driver " val url = " jdbc:impala://host:21050;AuthMech=1;KrbRealm=EXAMPLE.COM;KrbHostFQDN=host;KrbServiceName=impala " Class.forName(driverName) val conn = DriverManager.getConnection(url) val prst = conn.prepareStatement( " select * from database.movieas

get latest data from hive table with multiple partition columns

由 风流意气都作罢 提交于 2021-02-19 05:36:06
问题 I have a hive table with below structure ID string, Value string, year int, month int, day int, hour int, minute int This table is refreshed every 15 mins and it is partitioned with year/month/day/hour/minute columns. Please find below samples on partitions. year=2019/month=12/day=29/hour=19/minute=15 year=2019/month=12/day=30/hour=00/minute=45 year=2019/month=12/day=30/hour=08/minute=45 year=2019/month=12/day=30/hour=09/minute=30 year=2019/month=12/day=30/hour=09/minute=45 I want to select

Optimizing Hive GROUP BY when rows are sorted

由 北城余情 提交于 2021-02-19 05:31:39
问题 I have the following (very simple) Hive query: select user_id, event_id, min(time) as start, max(time) as end, count(*) as total, count(interaction == 1) as clicks from events_all group by user_id, event_id; The table has the following structure: user_id event_id time interaction Ex833Lli36nxTvGTA1Dv juCUv6EnkVundBHSBzQevw 1430481530295 0 Ex833Lli36nxTvGTA1Dv juCUv6EnkVundBHSBzQevw 1430481530295 1 n0w4uQhOuXymj5jLaCMQ G+Oj6J9Q1nI1tuosq2ZM/g 1430512179696 0 n0w4uQhOuXymj5jLaCMQ G

Does Hive preserve file order when selecting data

由 不打扰是莪最后的温柔 提交于 2021-02-19 04:05:44
问题 If I do select * from table1; in which order data will retrieve File order Or random order 回答1: Without ORDER BY the order is not guaranteed. Data is being read in parallel by many processes (mappers), after splits were calculated, each process starts reading some piece of file or few files, depending on splits calculated. All parallel processes can process different volume of data and running on different nodes, the load is not the same each time, so they start returning rows and finishing

How can I get result format JSON from Athena in AWS?

由 白昼怎懂夜的黑 提交于 2021-02-19 03:18:58
问题 I want to get result value format JSON from Athena in AWS. When I select from the Athena then the result format like this. {test.value={report_1=test, report_2=normal, report_3=hard}} Is there any way to get JSON format result without replacing "=" to ":" ? The column format is map<string,map<string,string>> 回答1: select mycol from mytable ; +--------------------------------------------------------------+ | mycol | +--------------------------------------------------------------+ | {test.value=

How can I get result format JSON from Athena in AWS?

由 不羁岁月 提交于 2021-02-19 03:17:38
问题 I want to get result value format JSON from Athena in AWS. When I select from the Athena then the result format like this. {test.value={report_1=test, report_2=normal, report_3=hard}} Is there any way to get JSON format result without replacing "=" to ":" ? The column format is map<string,map<string,string>> 回答1: select mycol from mytable ; +--------------------------------------------------------------+ | mycol | +--------------------------------------------------------------+ | {test.value=

「分布式技术专题」三种常见的数据库查询引擎执行模型

由 元气小坏坏 提交于 2021-02-18 20:46:44
注： 本文涉及到的相关资料图片摘自 CARNEGIE MELLON DATABASE GROUP 发表的 CMU SCS 15-721 (Spring 2019) :: Query Execution & Processing （点击可查看） 1. 迭代模型/火山模型（Iterator Model） 又称 Volcano Model 或者 Pipeline Model 。 该计算模型将关系代数中每一种操作抽象为一个 Operator，将整个 SQL 构建成一个 Operator 树，查询树自顶向下的调用next()接口，数据则自底向上的被拉取处理。 火山模型的这种处理方式也称为拉取执行模型（Pull Based）。 大多数关系型数据库都是使用迭代模型的，如 SQLite、MongoDB、Impala、DB2、SQLServer、Greenplum、PostgreSQL、Oracle、MySQL 等。 火山模型的优点在于：简单，每个 Operator 可以单独实现逻辑。 火山模型的缺点：查询树调用 next() 接口次数太多，并且一次只取一条数据，CPU 执行效率低；而 Joins, Subqueries, Order By 等操作经常会阻塞。 2. 物化模型（Materialization Model） 物化模型的处理方式是：每个 operator 一次处理所有的输入

Reg : Efficiency among query optimizers in hive

由 做~自己de王妃 提交于 2021-02-18 18:13:30
问题 After reading about query optimization techniques I came to know about the below techniques. 1. Indexing - bitmap and BTree 2. Partitioning 3. Bucketing I got the difference between partitioning and bucketing, and when to use them but I'm still confused how indexes actually work. Where is the metadata for index is stored? Is it the namenode which is storing it? I.e., actually while creating partitions or buckets we can see multiple directories in hdfs which explains the query performance

Reg : Efficiency among query optimizers in hive

由 早过忘川 提交于 2021-02-18 18:13:25
问题 After reading about query optimization techniques I came to know about the below techniques. 1. Indexing - bitmap and BTree 2. Partitioning 3. Bucketing I got the difference between partitioning and bucketing, and when to use them but I'm still confused how indexes actually work. Where is the metadata for index is stored? Is it the namenode which is storing it? I.e., actually while creating partitions or buckets we can see multiple directories in hdfs which explains the query performance
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