Hive0.8.1版本与HBase0.94.0集成的问题总结和参考文献
hbase实训总结
hbase实训总结HBase实训总结在过去的两周里，我们进行了一项关于HBase的实训。
HBase是一个分布式的、版本化的、非关系型数据库，它提供了高可靠性、高性能的数据存储服务。
在这次实训中，我们深入学习了HBase的基本概念、架构、数据模型以及如何进行数据操作。
以下是我对这次实训的总结。
一、实训内容1. HBase基本概念和架构我们首先学习了HBase的基本概念，包括表、行、列、单元格等。
我们还了解了HBase的架构，包括HMaster、RegionServer、Zookeeper等组件的作用和工作原理。
2. HBase数据模型HBase的数据模型是其核心特性之一。
我们学习了HBase的数据模型，包括表的创建、删除、修改，行和列的添加、删除、修改等操作。
我们还学习了HBase的过滤器、排序和聚合等高级特性。
3. HBase数据操作在实训中，我们通过编程语言（如Java）进行了HBase的数据操作。
我们学习了如何连接到HBase，如何创建表，如何插入、读取、更新和删除数据等操作。
我们还学习了如何使用HBase的API进行复杂的数据查询和操作。
二、遇到的问题和解决方案在实训过程中，我们遇到了一些问题，但通过团队的合作和努力，我们成功地解决了它们。
其中一些问题包括：连接HBase时出现连接错误、数据插入失败、数据查询结果不正确等。
为了解决这些问题，我们查阅了相关文档和资料，并在团队成员之间进行了深入的讨论和交流。
最终，我们找到了问题的根源，并采取了相应的解决方案。
三、收获和感想通过这次实训，我深入了解了HBase的原理和应用，掌握了HBase的基本操作和高级特性。
我学会了如何使用Java编程语言进行HBase的数据操作，包括表的创建、数据的插入、读取、更新和删除等操作。
此外，我还学会了如何使用HBase的API进行复杂的数据查询和操作，如过滤器、排序和聚合等。
在实训过程中，我深刻体会到了团队合作的重要性。
hive和hbase整合的原因和原理
带来的应用层面好处是降低了技术门槛，技术人员可以使用类Sreadpropertyexternalsignedinofnull请尝试刷新页面或更换浏览器重试
hive和 hbase整合的原因和原理
为什么要进行hive和hbase的整合？ hive是高延迟、结构化和面向分析的； hbase是低延迟、非结构化和面向编程的。 Hive集成Hbase就是为了使用hbase的一些特性。或者说是中和它们的特性。
hive大课程的心得感悟
hive大课程的心得感悟摘要：1.引言2.Hive简介3.大课程学习心得4.技术收获与实战应用5.总结与展望正文：【引言】在现今大数据时代，掌握一门数据处理技术显得尤为重要。
作为一名热衷于大数据技术的学习者，我有幸参加了Hive大课程的学习。
在此，我将分享我的学习心得感悟，希望能给大家带来一定的启示。
【Hive简介】Hive是一个基于Hadoop的数据仓库工具，可以用来进行数据提取、转换和加载（ETL）。
它允许用户使用类SQL的语言（HiveQL）进行数据查询，从而简化大数据处理过程。
Hive适用于海量数据的处理，已经在众多企业级应用中得到了广泛应用。
【大课程学习心得】在Hive大课程中，我深入了解了HiveQL的语法、数据存储结构、数据处理流程等。
通过学习，我对大数据处理有了更清晰的认识。
以下是我在学习过程中的一些心得：1.明确数据处理需求：在学习Hive之前，首先要明确自己的数据处理需求，以便更好地利用Hive进行数据处理。
2.熟悉HiveQL语法：HiveQL与传统SQL语法相似，但有一些特性和语法需要注意。
熟练掌握HiveQL语法可以提高工作效率。
3.了解数据存储结构：Hive支持多种数据存储格式，如Parquet、ORC 等。
了解各种存储格式的优缺点，可以根据实际需求选择合适的存储格式。
4.掌握数据处理流程：Hive的数据处理流程包括数据导入、数据处理和数据导出。
了解这些流程可以帮助我们更好地优化数据处理性能。
【技术收获与实战应用】通过学习Hive大课程，我收获颇丰。
在实际项目应用中，我运用所学知识，成功完成了数据处理任务。
以下是一些实战应用案例：1.数据清洗：利用HiveQL对原始数据进行筛选、去重和转换等操作，提高数据质量。
2.数据仓库建设：基于Hive搭建数据仓库，实现数据的统一存储和管理，便于数据分析和挖掘。
3.数据报表：利用HiveQL生成数据报表，为业务决策提供数据支持。
4.数据挖掘与分析：结合其他大数据技术，如Python、Spark等，对数据进行挖掘和分析，发现潜在价值。
hive与hbase通信原理
hive与hbase通信原理
Hive与HBase通信原理的实现是利用两者本身对外的API接口互相通信来完成的。
这种相互通信是通过$HIVE_HOME/lib/hive-hbase-handler-.jar
工具类实现的。
具体步骤如下：
1. Hive通过HBaseStorageHandler，可以获取到Hive表所对应的HBase 表名、列簇和列、InputFormat、OutputFormat类、创建和删除HBase
表等。
2. Hive访问HBase中HTable的数据，实质上是通过MapReduce读取HBase的数据，其实现是在MR中，使用HiveHBaseTableInputFormat
完成对HBase表的切分，获取RecordReader对象来读取数据。
对HBase 表的切分原则是一个Region切分成一个Split，即表中有多少个Regions，MR中就有多少个Map。
读取HBase表数据都是通过构建Scanner，对表进行全表扫描，如果有过滤条件，则转化为Filter。
当过滤条件为rowkey 时，则转化为对rowkey的过滤。
Scanner通过RPC调用RegionServer
的next来获取数据。
以上内容仅供参考，如需更多信息，建议查阅相关文献或咨询相关技术人员。
hive面试知识点总结
hive面试知识点总结Hive是一个用于存储、查询和分析大规模数据集的数据仓库系统。
它建立在Hadoop的基础上，提供了类似于SQL的查询语言，使得对分布式存储系统中的数据进行查询和分析变得更加方便。
在Hive的面试中，候选人需要展示对Hive的深入了解和熟练掌握，包括HiveQL查询语言、Hive数据分区、Hive表和视图的创建和管理、Hive性能优化等方面的知识。
下面是一些常见的Hive面试知识点总结：HiveQL查询语言HiveQL是Hive的查询语言，类似于SQL语言，用于对Hive中存储的数据进行查询和分析。
在面试中，候选人需要展示对HiveQL语法的熟练掌握，包括常见的查询语句、聚合函数、子查询、连接操作等方面的知识。
候选人可以通过展示编写复杂查询、优化查询性能等方式来展示对HiveQL语言的熟练掌握。
Hive数据分区Hive支持对数据进行分区，可以基于数据的某个字段进行分区，提高查询性能。
在面试中，候选人需要展示对Hive数据分区的理解和应用，包括如何将数据进行分区、如何查询分区数据、如何管理分区等方面的知识。
候选人可以通过展示对分区数据的查询和管理操作来展示对Hive数据分区的熟练掌握。
Hive表和视图的创建和管理Hive支持创建和管理表和视图，用于对数据进行存储和查询。
在面试中，候选人需要展示对Hive表和视图的创建和管理的理解和应用，包括如何创建表和视图、如何添加和删除表的列、如何修改表的属性、如何查询视图数据等方面的知识。
候选人可以通过展示创建和管理表和视图的操作来展示对Hive表和视图的熟练掌握。
Hive性能优化Hive在处理大规模数据时需要考虑性能优化的问题。
在面试中，候选人需要展示对Hive性能优化的理解和应用，包括如何优化查询性能、如何提高数据加载性能、如何减少数据存储空间等方面的知识。
候选人可以通过展示优化查询性能、提高数据加载性能等操作来展示对Hive性能优化的熟练掌握。
高效的Hive与Hbase多表连接引擎说明书
International Conference on Applied Science and Engineering Innovation (ASEI 2015)An efficient Join-Engine to the SQL query based on Hive with HbaseZhao zhi-cheng & Jiang YiInstitute of Computer Forensics, Chongqing University of Posts and Telecommunications,Chongqing 400065, ChinaKEYWORD: Hbase; Hive; multi-join; Join EngineABSTRACT. Hbase combine Hive application platform can analysis of huge amounts of data easi-ly. And querying on multi-join is the bottleneck of restricting the performance. To solve this prob-lem designing a Join Engine between Hbase and Hive. It can read Hbase data and complete multiple table joins and optimization in advance to queries. Using the Join Engine can reduce the time of mapreduce process to data sort and shuffle, effectively improve the efficiency of Hbase combine Hive queries. The experimental results show that the solution with Join Engine can obviously short-en the query time in multi-table join, support the Hbase quasi real-time application.1INSTRUCTIONWith the development of Internet, the growth of the data presented crazy. Huge unstructured data has already far more than the traditional structured data. The traditional databases have been unable to meet the requirements. How to read and analysis the large amounts of unstructured data efficient-ly become the issues of common concern. Distributed column type database Hadoop Database (Hbase) [1] not only has the advantage of high expansibility and large capacity but also has hadoop platform support. So, Hbase become very popular. But it does not support the SQL query [2] [3]. Restrict the development of Hbase. At present, Hbase use Hive query mechanism to solve the prob-lem.1.1Relative workHBase community puts forward two kinds of composite architectural solutions to solve the prob-lem of Hbase do not support SQL queries. One of them is the integration of the MySQL framework [4]. The second is integration framework with Hive. The first solutions add a MySQL layer which has an indirect links with HBase. When the data in HBase has updated, the scheme must restore the HBase data to MySQL database for SQL queries. The second scheme use HBaseIntegration to es-tablish a directly connected between Hbase and Hive. Hive support real-time query under Hbase da-tabase updates. Hive is an open source data warehouse project on Hapdoop platform [5]. It can parse SQL sentence into graphs task execution and execute it with MapReduce task job. Hive is widely use in massive log analysis. The Hbase integrated with Hive scheme can make full use of graphs of parallel capability to provide users with easy operation of SQL query and analysis of data [6].So the scheme has been widely used in the era of big data web [7]. But the plan cannot avoid problem of multi-table join queries [8]. Many application choices change the sequence of the data before join. Although the performance has a little improved, but the scheme must know the SQL sentence before optimizing the order of join data [10].In conclusion, Hbase integrated with Hive scheme is optimal solution to SQL query on Hbase. But the MapReduce job cost so much time that the scheme cannot use in real time or quasi real time application.2THE HBASE AND HIVE INTEGRATION FRAMEWORKHive establishes one-to-one relationship with the Hbase original table. Hive parse SQL queries and run the MapReduce task.Figure 1Hive and HBase integrationAs shown in figure 1, The Hbase application programming interface HbaseStorageHandler sup-port the one-to-one relationship with Hive. Hive has the responsibility to parse SQL sentence and execute the query task. HBase provide the storage capacity.The basic process Hbase integration Hive SQL query is first of all establish an association be-tween Hbase and Hive, then parsing and decomposed SQL statement to execution plan by Hive, at last excute Mapreduce task, get the result return to Hbase and clients. Detail of the process is as fol-lows:1) Hive and Hbase corresponding mapping relationship must be established before the query. Hive's usually in contact with the client side to establish the appearance of establishing to Hbase physical storage. After mapping, Hive can read and write data which store in Hbase.2) Hive parse SQL statements to execution plan: When Hive client receiving the user's SQL statement Hive parser convert the statement into ASTNode types of syntax tree, and then generate operator tree / DAG, make some optimization operation, then take the tree / DAG generate sever-al MapReduce tasks.3) Compile the task information generated serialization stage to plan.xml, then start map-reduce, read from Hbase corresponding data input to map function. When configure deserialization plan.xml.4) After the map function and reduce function returns a result set, save the results output as Col-lector class, set the file type to save the results by Record Writer. HiveBaseResultSet class will then update the results to HBase data and display to the client.3HBASE HIVE QUERY OPTIMIZATIONHbase Hive query mechanism is mainly composed of Hbase and Hive connection Sql parsing and Mapreduce jobs three modules. Mapreduce job is the most time-consuming process. Because the table join operation will completely processes by mapjoin function in MapReduce tasks, it takesa lot of time.3.1Multi-table query problem analysisA multi-table join query is very effective data analysis methods, but Hbase does not support mul-tiple table queries. Although with Hbase Hive fusion can solve this problem, but the multi-table query efficiency has become a new challenge. At present most of the commercial application and research only adjust the join order to improve the query efficiency. Some low efficiency SQL statements even cause the process collapse. The problem is now the multi-table connection in MapReduce tasks needs a lot of process time and more task jobs.For instance three tables join in a query will generate at least two MR jobs. SELECT a.val, b.val, c.val FROM a JOIN b ON (a.key = b.key1) JOIN c ON (c.key = b.key2) Three tables connection with two keys. How doses MapReduce task connect the three tables? In first job table A and table B based on first key connect. Results of last job and table c based on the second key connect in second map join task. The SQL query needs to wait for two MR jobs for tables’ connection. This became the most time-consuming process in Hbase Hive integration query.3.2Hbase and Hive integration optimizationDue to Hbase column structure, each Hbase table column of the cluster is given priority to one or two. If the column clusters too much, Hbase will be affected read and write speed. In column type database the read and write unit is one column. So it is not necessary to store too much columns in a single table. Hbase compared with the traditional database can store more redundant data. Duplica-tion of the column data in a multi-table join queries occur frequently equijoins. Utilizing the princi-ple of pre-processing and cache this article designs a Join-Engine which pre-establish a connection on multi-table and sort the data after optimization. When a client queries arrive, you can quickly get the results directly in the Join-Engine, thus greatly improving the efficiency of client queries. In practical application is unable to determine whether the tables should read into the Join-Engine. If Join-Engine read some tables based some rules, but the user never requested, then the Join-Engine is not worth. Because queries have memory characteristic that query often repeat on the same sys-tem. It is best to read the related tables which are appear in the query history.3.3Join-EngineBecause of the separation of multi-table joining with query operations and utilizing the principle of locality and log analysis, the article change the traditional Hbase and Hive integration framework add the Join-Engine as shown in Figure 2.Figure 2 Join-EngineJoin-Engine consists of two modules, Pre-reading mechanisms: Read the relevant data from Hbase based on the query history in Hive; Optimized connection: Complete multi-table join opera-tions, and sort optimize data before Mapreduce upgrade query efficiency.Pre-reading module select the table in Hbase by query history record. In practical systems can also manually configure achieve more accurate results. Optimized connection module sort and merge tables in units of data. After adding Join-Engine, (1) Mapjoin operation which is most time-consuming is complete before the SQL queries come. Conversely without Join-Engine, First you need to generate a small table Hash Table file and distributed to Cache. Then MapJoinOperator load small table (Hash Table) from the Distributed Cache execute map Join operations. So the connec-tion operation for Mapreduce process is very complex and time-consuming work. (2) Join-Engine carry on sort optimization process when connect operation is in progress. Then the map input split process can be achieved optimal effect. Similar data are assigned to the same slice task statistics, which can speed up the cleaning work after the map statistics. Conversely without Join-Engine, Then fragments will be random load into the map function. Prior to Reduce function get the data, the data need to go through the process of data cleansing – shuffle. Shuffle process is constantly read the data into the RAM carry out sorting and merging operations. So join the pre-optimized processing can effectively reduce the cleaning process of sorting and merging. Query time can be shortened.3.4Optimization Hbase and Hive SQL queryThe SQL query process of Hbase and Hive integration added Join-Engine as shown in Figure 3，Figure 3 SQL query proce ssThere are two ways to process the SQL query. The one is MapReduce job directly read data from the Join-Engine. Another is read data from Hbase original tables.4THE DESIGN OF EXPERIMENT4.1The data source of experimentFigure 4 SQL query timeThe data source of experiment comes from two custom data sets A and B. They have the same structure: table a<column family: name, column family id>.table b <column family id, column fam-ily motive>.table c <column family name, column family tag>. At sets A there are a lot of the same data between two tables in the column of id or name. Sets B have scarcely duplicate data.4.2The results and analysis of experimentExperimental Methods: Contrast SQL query time between added Join-Engine and without Join-Engine. Take 10 groups SQL query comparative experiments, each set of experiments carried out ten times the mean value. The 1-3 group are three multi-table queries experiments, use sets A; 4-6 group experiment is corresponds with 1-3 group use the same SQL queries, but using the dataset B, 7-10 group SQL experiments using data sets A, non-multi-table join queries. As shown in Figure 4 As shown, the 1-3 group experiments have very large difference between after added Join-Engine and old solution. But while using the same SQL query statements the 4-6 groups have littledifference on SQL query time. The only difference is the data sets. The data set A has a lot of du-plicate data so that it will produce more results at mapJoin process. The data set B will generate much less data at mapJoin because it has less correlation data. Because of each 100 000 associated data of table input will return billions of data need so much memory and CPU compute time. The value associated with the data set B does not exist, in the multi-table joins returns the result is zero, consuming very little. So the efficiency of Join-Engine is closely related to the data set. When the data set has more associated column values, The Join-Engine has more efficiency on multi-table SQL query. The 7-10 groups’ experiments shown the Join-Engine has less influence on single table SQL query.Experiment 2:，Sorted data has impact on Mapreduce query efficiency. Use two contains the same 100 000 data tables, one is sorted, and another is an unordered table. Contrast their sql query time. The results shown in Figure 5：Figure5 sort and unsort data query time comparisonAs shown in Figure 2-4 experiments displays the sorted data source has better performance in the implementation of Mapreduce. Because the 2-4 SQL queries has some conditions the result of SQL queries are sorted. SO the reduce function has to sort the data. So the sorted source data can effectively reduce Mapreduce work of sorting data. The first group of SQL test is statement as se-lect *, then reduce function does not need to sort the results, so Mapreduce processing performance has no connection with source data.The result of experiment shows that，Add Join-Engine can effectively save the mapjoin time from Mapreduce operation. In the case of highly relevant data sets Join-Engine can effectively re-duce the time multi-table join queries Because the higher correlation datasets the more Join-Engine works preprocess. That means add Join-Engine will be able to better support the quasi-realtime SQL query applications, the more obvious the effect of Preprocessing mechanism; Pre-sorting pro-cess can be reduced when the conditions are in the process of inquiry Mapreduce sort operation, ef-fectively reducing Mapreduce time. Therefore, Join-Engine can improve query efficiency from two aspects, reducing Mapreduce time of mapjoin operations and sorting operations.5CONCLUSION AND FUTURE WORKIn the development of the integration technology of Hbase and Hive，Multi-table query perfor-mance problems become the key factor to restrict the development of this technology. This article provides a Hbase and Hive integration optimization which added a Join-Engine pre-process Multi-table joining and sorting. Experimental results show that, Added Join-Engine hive higher efficient on SQL query especially on multi-table join query. The next step will continue to optimize the de-sign of the Join-Engine, allowing to the original data analysis capabilities can have an adaptive work on any Hbase database.6REFERENCES[1]HBase[EB/OL]. /[2]J.Kennedy,IndexedTransactionalHBasemaintained[EB/OL],https:///hbase-trx/, 2011.[3]L. George, HBase-The Defensive Guide, Sebastopol[R]:O'Reilly Media, 2011.[4]HBaseIntegrtion[EB/OL] .https:///confluence/display/Hive/HbaseIntegration/[5]Hive[EB/OL].//lib/view/open4.html[6]ZHAO long, JIANG Rong-an. Research of massive searching logs analysis system based onHive [J]. Application Research of Computers, 2013, 30(11): 3343-3345.[7]Doshi K A, Zhong T, Lu Z, et al. Blending SQL and NewSQL Approaches: Reference Archi-tectures for Enterprise Big Data Challenges[C]//Cyber-Enabled Distributed Computing and Knowledge Discovery (Cyber), 2013 International Conference on. IEEE, 2013: 163-170.[8]Barbierato E, Gribaudo M, Iacono M. Performance evaluation of NoSQL big-data applicationsusing multi-formalism models [J]. Future Generation Computer Systems, 2014, 37: 345-353. [9]WANG Mei, XING Lulu, SUN Li. MapReduce Based Heuristic Multi-Join Optimization underHybrid Storage [J]. Journal of Frontiers of Computer Science and Technology, 2014, 8(11): 1334-1344.[10]W ANG Jing, WANG Teng-jiao, Yang Dong-qing, Li Hongyan. A Filter-Based Multi-Join Al-gorithm in Cloud Computing Environment [J]. Journal of Computer Research and Develop-ment, 2011 (S3): 245-253.。
HBase数据可用性和持久性研究与实现的开题报告
HBase数据可用性和持久性研究与实现的开题报告一、选题背景和意义随着大数据时代的到来，传统的关系型数据库已不能满足海量数据的存储和处理需求，出现了一系列分布式数据库系统，如HBase、Cassandra、MongoDB等。
HBase是一种基于Hadoop分布式文件系统HDFS的列式数据库，以其可伸缩性、高可用性等优势，在互联网、金融、电信、物流等行业得到广泛应用。
在这些行业中，数据的可用性和持久性是至关重要的，应用程序需要随时随地访问数据，同时也需要保证数据的安全性和完整性。
因此，在HBase中，数据的可用性和持久性是非常重要的。
本研究将从数据的可用性和持久性两个方面出发，探讨如何提高HBase的数据可用性和持久性，以便更好地保证数据的安全性和完整性。
二、研究内容和方法本研究将从以下两个方面进行研究：1. HBase数据可用性研究在HBase中，数据可用性的保证包括两个方面：（1）RegionServer的可用性RegionServer是HBase中最为重要的组件之一，它负责管理HBase 中的数据区域（Region），对于RegionServer的故障，将会导致相应Region的不可用，甚至数据的丢失。
因此，如何提高RegionServer的可用性是非常重要的。
（2）数据在Region之间的分布均衡HBase是一个列族数据库，其中的列被组织到列族中，每个列族都对应一个或多个Region。
在HBase中，Region的负载均衡是非常重要的，如果某个Region的数据过多，将会导致该Region的访问变慢，甚至崩溃。
2. HBase数据持久性研究在HBase中，数据持久化是指将数据写入磁盘，防止数据丢失，保证数据的可靠性。
数据持久化也是增加HBase稳定性的重要手段。
本研究将从以下两个方面进行研究：（1）HBase数据写入磁盘机制的改进HBase的数据持久化通过WAL（Write-Ahead-Log）机制实现，WAL 是防止数据丢失的重要手段。
Hive和HBase的区别
Hive和HBase的区别Hive和HBase的区别Hive是为了简化编写MapReduce程序⽽⽣的，使⽤MapReduce做过数据分析的⼈都知道，很多分析程序除业务逻辑不同外，程序流程基本⼀样。
在这种情况下，就需要Hive这样的⽤⼾编程接⼝。
Hive本⾝不存储和计算数据，它完全依赖于HDFS和MapReduce，Hive中的表纯逻辑，就是些表的定义等，也就是表的元数据。
使⽤SQL实现Hive是因为SQL⼤家都熟悉，转换成本低，类似作⽤的Pig就不是SQL。
HBase为查询⽽⽣的，它通过组织起节点內所有机器的內存，提供⼀個超⼤的內存Hash表，它需要组织⾃⼰的数据结构，包括磁盘和內存中的，⽽Hive是不做这个的，表在HBase中是物理表，⽽不是逻辑表，搜索引擎使⽤它來存储索引，以满⾜查询的实时性需求。
hive类似CloudBase，也是基于hadoop分布式计算平台上的提供data warehouse的sql功能的⼀套软件。
使得存储在hadoop⾥⾯的海量数据的汇总，即席查询简单化。
hive提供了⼀套QL的查询语⾔，以sql为基础，使⽤起来很⽅便。
HBase是⼀个分布式的基于列存储的⾮关系型数据库。
HBase的查询效率很⾼，主要由于查询和展⽰结果。
hive是分布式的关系型数据库。
主要⽤来并⾏分布式处理⼤量数据。
hive中的所有查询除了"select * from table;"都是需要通过Map\Reduce 的⽅式来执⾏的。
由于要⾛Map\Reduce，即使⼀个只有1⾏1列的表，如果不是通过select * from table;⽅式来查询的，可能也需要8、9秒。
但hive⽐较擅长处理⼤量数据。
当要处理的数据很多，并且Hadoop集群有⾜够的规模，这时就能体现出它的优势。
通过hive的存储接⼝，hive和Hbase可以整合使⽤。
1、hive是sql语⾔，通过数据库的⽅式来操作hdfs⽂件系统，为了简化编程，底层计算⽅式为mapreduce。
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[hadoop@hadoop hive]$ hive
/usr/local/hadoop/hive/bin/hiv e: line 72: [: /usr/local/hadoop/hive/lib/hive-exec-0.8.1.jar: binary operator expected
/usr/local/hadoop/hive/bin/hiv e: line 82: [: /usr/local/hadoop/hive/lib/hive-metastore-0.8.1.jar: binary operator expected
/usr/local/hadoop/hive/bin/hiv e: line 88: [: /usr/local/hadoop/hive/lib/hive-cli-0.8.1.jar: binary operator expected
/usr/local/hadoop/hive/bin/ext/util/execHiveCmd.sh: line 21: [: /usr/local/hadoop/hiv e/lib/hive-cli-0.8.1.jar: binary operator expected
Exception in thread "main" ng.ClassNotFoundException: /usr/local/hadoop/hive/lib/hiv e-cli-0/9/0-SNAPSHOT/jar at jav ng.Class.forName0(Native Method)
at jav ng.Class.forName(Class.java:247)
at org.apache.hadoop.util.RunJar.main(RunJar.jav a:149)
在H ive的Lib目录下多拷贝了一下名称前缀相同的Jar文件，导致hive脚本中if语句判断出错，删除多余的j ar包后，解决。
网上找到的原因
thats because /myfilelocationpath/temp/bingof ile* will return more than one f ile name and if can't test all at once.. so its better go f or f or loop..
参考官方文档的描述，带参数启动hive，注意中划线—的使用
$HIVE_HOME/bin/hive --auxpath $HIVE_HOME/lib/hive-hbase-handler-0.9.0.jar,
$HIVE_HOME/lib/hbase-0.92.0.jar, $HIVE_HOME/lib/zookeeper-3.3.1.jar -hiveconf
hbase.master=localhost:60000
参考文献：
https:///confluence/display/Hive/HowToContribute
https:///confluence/display/Hive/GettingStarted+EclipseSetup
CentOS安装TortoiseSVN svn 客户端
/bsdgo/blog/item/cc144fd62ca094dc50da4bae.html
linux centos 安装eclipse
/yuzhi2217/blog/item/eb7f2ff4157ada70dcc47455.html
Installing Apache Ant
/manual/install.html#librarydependencies
http://i-proving.ca/space/Technologies/Ant+Tutorial
官方关于Hive和HBASE集成的文档描述
https:///confluence/display/Hive/HBaseIntegration
$HIVE_SRC/build/dist/bin/hive
Apache Dow nload Mirrors
/dyn/closer.cgi/hadoop/common/
Hive的官方起步文档
https:///confluence/display/Hive/GettingStarted
hive没有直接插入一条数据的s ql，不过可以通过其他方法实现：
假设有一张表B至少有一条数据，我们想向表A（int，s tring）中插入一条数据，可以用下面的方法实现：
from B
ins ert table A select 1，‘abc’ limit 1；。