Hive Query Running Slow

The big catch is that even though it provides an SQL like querying environment, it uses the MapReduce methodology in the background to query the database and return results. Presto’s query optimizer is unable to improve queries where several LIKE clauses are used. Hive 3 new features. SELECT * FROM precipitation_data; Indexing. Pig vs Hive: Benchmarking High Level Query Languages Benjamin Jakobus IBM, Ireland Dr. PSI-BLAST allows the user to build a PSSM (position-specific scoring matrix) using the results of the first BlastP run. LLAP enables application development and IT infrastructure to run queries that return real-time. Say if a business requirement stores the data of this table in GZIP format, then a dependent process, running a hive query on this data would spin up 1500 mappers to process individual splits for each file, as the GZIP format is non splittable. Still if you need quick result, you have to login to impala-shell instead of Hive and run your query. Self-service exploratory analytics is one of the most common use cases we see by our customers running on Cloudera's Data Warehouse solution. In late 2016 and in 2017, I entered our Hadoop environment, and started to use Hive, Impala, Spark SQL to query HDFS data extensively for my analytical projects. Best Practices. 3 Benefits of Apache Hive View 2. Spark: Spark 1. Even if there is an index on the appointment_date column in the table users, the query will still need to perform a full table scan. processing (LLAP) can improve the performance of interactive queries. With Presto, the social media giant gave itself a way to query its 300-petabyte data warehouse spread across a massive distributed. The new and changed features and documentation updates are described in the following sections. Big Data maybe different, like Aster,Hive, Pig etc. One use of this could be to put a class. The commands in SQL are called Queries and they are of two types. Menu Compressing Text Tables In Hive 01 June 2011 on hadoop, hive, ruby At Forward we have been using Hive for a while and started out with the default table type (uncompressed text) and wanted to see if we could save some space and not lose too much performance. 0 Apache Hive 2. Linguistic Data Consortium corpora available via the SALTS Lab; Rutgers Digital Humanities Initiative. Configure Hive Connector properties for Generated SQL. The help desk or database team usually hears that described as the application is slow or the database is slow. First of all, Hive can be configured to use Spark as the query execution engine if your Hive platform supports Hive on Spark. Yet many queries run on Hive have filtering where clauses limiting the data to be retrieved and processed, e. The Hive query execution engine converted this query into MapReduce jobs. So, I look at Hive as a really accessible way. The Hortonworks Hive ODBC Driver with SQL Connector interrogates Hive to obtain schema information to present to a SQL-based application. mode is set to strict, then you need to do at least one static partition. If using default Spark in IOP. The Hive Query Language is a subset of SQL-92. On the whole, Hive on MR3 is more mature than Impala in that it can handle a more diverse range of queries. QuerySurge Database Backup Procedures QuerySurge is backed by a MySQL database. Sometimes Amazon Redshift takes hours together to just drop or truncate the tables even if table has very limited rows. BlastP simply compares a protein query to a protein database. Note: Before you can connect to an Oracle database using Power Query, you need the Oracle client software v8. In late 2016 and in 2017, I entered our Hadoop environment, and started to use Hive, Impala, Spark SQL to query HDFS data extensively for my analytical projects. registerTempTable("comments"), so we can run SQL queries off of it. The maximum size of the result set from a join query is the product of the number of rows in all the joined tables. Similarly Hive on Tez in HDP 3. Both were small networks. Spark: Spark 1. My computer was slow looked for solutions on the internet ran across DriverHive that updated my drivers and my computer is now running much faster! Thank you DriverHive!” —phuocvtn88 “After having issues with my PC a friend said I should update my drivers. (Refer to Software Preparation in Cloudera -Hive Setup) HDP: Hive 1. Send log file with remote_syslog2. One of the common support requests we get from customers using Apache Hive is –my Hive query is running slow and I would like the job/query to complete much faster – or in more quantifiable terms, my Hive query is taking 8 hours to complete and my SLA is 2 hours. Spark (and Hadoop/Hive as well) uses “schema on read” – it can apply a table structure on top of a compressed text file, for example, (or any other supported input format) and see it as a table; then we can use SQL to query this “table. It also uses standard ANSI SQL, which Kramolisch said is easier to learn than the Hive Query Language and its “lots of hidden gotchas. If you want to run serious JDBC applications, i. This information is used to find data so the distributed resources can be used to respond to queries. Window aggregate functions (aka window functions or windowed aggregates) are functions that perform a calculation over a group of records called window that are in some relation to the current record (i. Even after running it for hours. commit phase has been running for almost 16 hours and has not finished yet. Hive Performance - 10 Best Practices for Apache Hive June 26, 2014 by Nate Philip Updated July 13th, 2018 Apache Hive is an SQL-like software used with Hadoop to give users the capability of performing SQL-like queries on it's own language, HiveQL, quickly and efficiently. Upon receiving the query results, javascript on client browser will parse the data locally to visualize. Without partitioning, Hive reads all the data in the directory and applies the query filters on it. All the above functions are present in Apache Hive 0. method identifier in here to see where the slow query is coming from. It's applied when sleeping in most beds (there are some bed-type objects that instead give Regeneration, so sleeping in them does not stop food bar depletion). 4 is installed. SELECT * WHERE state=’CA’. Some background information: I'm working with Dataiku DSS, HDFS, and partitioned datasets. The time required to load the data into Hive was less than 1 minute. The correlated sub query has a simpler plan, does less i/o, and runs in 4. Filters and query-based input controls that rely on Hadoop-Hive data sources will be slow to populate the list of choices. Filters and query-based input controls that rely on Hadoop-Hive data sources will be slow to populate the list of choices. Mechanism: 1. As a consequence, the query execution can be slower than expected. My computer was slow looked for solutions on the internet ran across DriverHive that updated my drivers and my computer is now running much faster! Thank you DriverHive!” —phuocvtn88 “After having issues with my PC a friend said I should update my drivers. Finally, note in Step (G) that you have to use a special Hive command service (rcfilecat) to view this table in your warehouse, because the RCFILE format is a binary format, unlike the previous TEXTFILE format examples. why? i shouldn't have to analyze simple queries like this to find workarounds that make them reasonably performant. I'm not sure what the problem is, but seems to be a Hive performance issue when it comes to "highly partitioned" tables. Yet, you are waiting which very slow computer start off up or run multiple programs. If the partitions aren't stored in a format that Athena supports, or are located at different S3 paths, run the command ALTER TABLE ADD PARTITION for each partition. Reverse engineering from Hive database processing is slow due to the absence of system tables. Second, we are optimizing Hive's query execution plans and based on our initial changes, we have already seen query time drop by 90% on some of our test queries. After a few queries in Hive, I started to find Hadoop slow compared to my expectation (and slow compared to Netezza). A self join is a query that compares a table to itself. August 9, 2016. Impala is developed and shipped by Cloudera. Even after running it for hours. For example, some jobs that normally take 5 minutes are taking more than one hour. To apply the partitioning in hive , users need to understand the domain of the data on which they are doing analysis. All the above functions are present in Apache Hive 0. Earlier when i fire the same query it took around 5 minutes and now it is taking around 22 minutes. The wmf database includes filtered and preprocessed data. output property to true. The SQL AND condition and OR condition can be combined to test for multiple conditions in a SELECT, INSERT, UPDATE, or DELETE statement. My problem now is once I start to add larger files/more data, the queries are crazy slow. Slow changing dimensions. Presto vs Hive on MR3. How to monitor Netezza performance? Performance of Netezza depends on various factors such as distribution keys on the table, query performance, hardware factors such as number of spus, data skew i. An alternative to running ‘show tables’ or ‘show. One of the queries is: select a. We ask for a question, then wait for an answer. It is an alternative to using the Hive command line interface. Apache Hive is a library that converts SQL (to be precise, Hive SQL) into processing jobs that can be executed by various Hadoop-provided backends. Remember that the ORC file format is new as of Hive 0. Impala State Store - The state store coordinates information about all instances of impalad running in your environment. Hive Query Optimization params Date: September 27, 2014 Author: Ankit Bhatnagar 0 Comments Recently I was working a Hive Query and it is seeming running very slow. It explores possible solutions using existing tools to compact small files in larger ones with the goal of improving read performance. I have a particular job running (Hive query) which has two input datasets - one a very large, partitioned dataset, the other a small (~250 rows, 2 columns), non-partitioned dataset. Spark, Hive, Impala and Presto are SQL based engines. Keep your storage accounts and metastore database together as a unit in your application. In Hive Latency is high but in Impala Latency is low. Partitioning Tables Hive partitioning is an effective method to improve the query performance on larger tables. > Hive should only be used in cases where you can't fetch data from the CF > directly, using say CQL. One of the biggest challenges Hive users face is the slow response time experienced by end users who are running ad hoc queries. Have you noticed where the slowness happens? Is it within Hive itself, or is it just the MR job runs for a long time? If it is the MR job that slows everything down, please consider reducing the split size of the job and thus using more mappers to process the input data. See Query that produces a huge result is slow topic later in this article. Hive Compatibility − Run unmodified Hive queries on existing warehouses. (internal) Minimal increase rate in the number of partitions between attempts when executing take operator on a structured query. Improving or tuning hive… August 13, 2015 By Mohammad Farooq 1. on final output, intermediate data), we achieve the performance improvement in Hive Queries. ,Compute Speed - Hive will be my last option to query vs. 4 is installed. Spark SQL supports queries that are written using HiveQL, a SQL-like language that produces queries that are converted to Spark jobs. This means that when running an incorrect query (with incorrect or non-existing field names) the Hive tables will be populated with NULL instead of throwing an exception. Have you noticed where the slowness happens? Is it within Hive itself, or is it just the MR job runs for a long time? If it is the MR job that slows everything down, please consider reducing the split size of the job and thus using more mappers to process the input data. We are also looking at additional changes inside Hive's execution engine that we believe will significantly increase the number of records per second that a Hive task can process. Running Spark from the Command Line; Navigating “Big Data” with Spark and Hive; Rutgers Resources. don't use an obviously slow data format for Hive. When hive exec. How to determine the cause of a simple COUNT(*) query to run slow Eric Lin November 4, 2015 November 4, 2015 When a simple count query in Hive like below: SELECT COUNT(*) FROM table WHERE col = 'value'; with 2GB of data takes almost 30 minutes to finish in a reasonable sized cluster like 10 nodes, how do you determine the cause of the slowness?. If you continue browsing the site, you agree to the use of cookies on this website. Create a Job to Load Hive. Improving or tuning hive… August 13, 2015 By Mohammad Farooq 1. For all databases that I know, reading that volume is as close as 1 minute. As of Hive 1. If you have access to a server with SQL*Plus, you can run the query there in the background. slow to query• Often best to denormalize during load – Write once, read many. Without partitioning Hive reads all the data in the directory and applies the query filters on it. Presto AtScale, a maker of big data reporting tools, has published speed tests on the latest versions of the top four big data SQL engines. ” Still, Mayfield noted, it’s not as if everyone inside Airbnb, or any company, is going to be running SQL queries using Airpal — no matter how easy the tooling gets. table out of your query results. However such views in Hive used to be virtual and implied huge and slow queries. The table can then be. Without partitioning Hive reads all the data in the directory and applies the query filters on it. 2, we continue to improve the end user query experience with Hue, focusing on easier SQL query troubleshooting and increased compatibility with Hive. noconditionaltask - Whether Hive enable the optimization about converting common join into mapjoin based on the input file size. Apache Hive Table Design Best Practices Table design play very important roles in Hive query performance. Hive, Spark ) Ability to run ANSI SQL based queries against. When the query finishes, Hive doesn't terminate this spark application. 203e and Spark 2. > > Hive queries run in many minutes. To display the Query Editor dialog box, connect to a data source, and click Edit Query in the Navigator pane or double-click a query in the Workbook Queries pane. This means that when running an incorrect query (with incorrect or non-existing field names) the Hive tables will be populated with NULL instead of throwing an exception. Even at our data volume, relatively small for BigQuery’s standard, it can be worth investigating for those users who only run occasional analytics queries. Tez is also part of the execution engine for Hive LLAP. The native method is not available in MySQL 5. And start the custom spark-thrift server as below. Presto is an open-source distributed SQL query engine that is designed to run SQL queries even of petabytes size. One of the queries is: select a. This feature brings all 4 traits of database transactions -- Atomicity,Consistency,Isolation and Durability at row level, so that one application can add rows while another reads from the same partition without interfering with each other. To display the Query Editor dialog box, connect to a data source, and click Edit Query in the Navigator pane or double-click a query in the Workbook Queries pane. The SELECT statement is used to select data from a database. Note: all. select count(*) from foo limit 1 uses mapreduce and takes over a minute. Very Slow R - RImpala running Hello, I have run the below query in RStudio with RImpala package, and although the file size is relatively small (6 columns, about 800K observations), it's more than 10 hours and still running !. As long as the queries would have really returned the same plan, this is a big performance winner. Its takes more than 4 hours to complete. To resolve this, Hive supports parallel INSERT INTO values from the same session in the Hive version 2. The correlated sub query has a simpler plan, does less i/o, and runs in 4. Window aggregate functions (aka window functions or windowed aggregates) are functions that perform a calculation over a group of records called window that are in some relation to the current record (i. select count() from programs for example. The query I am running. stop the Spark ThriftServer from the Ambari console. All the above functions are present in Apache Hive 0. Be in control Everything is at your fingertips. Join Ben Sullins for an in-depth discussion in this video, Why use Hive, part of Analyzing Big Data with Hive. All the above functions are present in Apache Hive 0. Here you must run the command that generates the data to be loaded and the mysql commands either on separate terminals, or run the data generation process in the background (as shown in the preceding example). sortedmerge depending on the characteristics of the data Scenario 4 – The Shuffle process is the heart of a MapReduce program and it can be tweaked for performance improvement. This can happen due to a variety of reasons. Query using dplyr syntax. Earlier when i fire the same query it took around 5 minutes and now it is taking around 22 minutes. In this blog post, we'll discuss how to improve the performance of slow MySQL queries using Apache Spark. Impala State Store - The state store coordinates information about all instances of impalad running in your environment. 0 is the slowest on both clusters not because some queries fail with a timeout, but because almost all queries just run slow. Hive Query Running Slow. Avoid Exceeding Throughput. After the data is loaded, the query select * from should return data. To see which version of MySQL is installed, run: mysql -V. My issue is that returning the data to Power BI is extremely slow. The UNION operator is used to combine the result-set of two or more SELECT statements. In particular, it achieves a reduction of about 25% in the total running time when compared with Hive 3. A few, sometimes just one, of the reducers seem to run for much longer than the others. Impala is developed and shipped by Cloudera. For example, suppose that your data is located at the following S3 paths:. Sometimes Amazon Redshift takes hours together to just drop or truncate the tables even if table has very limited rows. But at the scale at which you'd use Hive, you would probably want to move your processing to EC2/EMR for data locality. When hive exec. Hive is extensible with UDFs. The required information is retrieved by manual parsing methods instead of a query language. how the data is distributed across the spus, etc There are different ways to check how your netezza box is performing. First things first: If you have a huge dataset and can tolerate some. Queries run at random using a jmeter test LLAP: Sub-Second Analytical Queries in Hive Massive improvement on slow storage with little memory cost 0 50 100 150. Still if you need quick result, you have to login to impala-shell instead of Hive and run your query. Skip to main content. Improving or tuning hive… August 13, 2015 By Mohammad Farooq 1. So I was able to get Hadoop 2. If using default Spark in IOP. In particular, it achieves a reduction of about 25% in the total running time when compared with Hive 3. Suppose the following table as the input. Once a file is added to a session, hive query can refer to this file by its name (in map/reduce/transform clauses) and this file is available locally at execution time on the entire hadoop cluster. If the partitions aren't stored in a format that Athena supports, or are located at different S3 paths, run the command ALTER TABLE ADD PARTITION for each partition. For query plan optimization to work correctly, make sure that the columns that are involved in joins, filters, and aggregates have column statistics and that hive. Hive allows only appends, not inserts, into tables, so the INSERT keyword simply instructs Hive to append the data to the table. Yet many queries run on Hive have filtering where clauses limiting the data to be retrieved and processed, e. How to run Hive queries through Hive Web Interface. just use impala. Such queries would need to join the User and Order tables with the Product table. Some background information: I'm working with Dataiku DSS, HDFS, and partitioned datasets. , with multiple concurrent users, with complex queries and on large datasets, we recommend you increase the memory and CPU allocation. One of the biggest challenges Hive users face is the slow response time experienced by end users who are running ad hoc queries. Your votes will be used in our system to get more good examples. but are very slow. So, I guess it. A few, sometimes just one, of the reducers seem to run for much longer than the others. Hive provides a database query interface to Apache Hadoop. This css script might be useful to all – It is not for syntax highlighting but works well for presenting the source code in original format :. Hive Query's are running slow hours! for a single wave of all 30 queries). In late 2016 and in 2017, I entered our Hadoop environment, and started to use Hive, Impala, Spark SQL to query HDFS data extensively for my analytical projects. Multi Table Inserts minimize the number of data scans required. Hive 3 new features. py and SQL_SELECT. So far we have seen running Spark SQL queries on RDDs. Hive and Impala implement different, disjointed subsets of what Apache Drill is capable of. Benchmark&Query&2 SELECT sourceIP, AVG(pageRank), SUM(adRevenue) AS earnings FROM rankings AS R, userVisits AS V ON R. So, they ask us how to improve the queries but it's hard work for us. Query or stored procedure: Optimize the logic of the query or stored procedure you specify in the copy activity source to fetch data more efficiently. run the query with SET. Once the data is loaded into the table, you will be able to run HiveQL statements to query this data. So, there are several Hive optimization techniques to improve its performance which we can implement when we run our hive queries. Big SQL uses in-memory caching, and can spill large data sets to the local disk at each node that is processing a query. With the recent release of Cloudera 6. See Query that produces a huge result is slow topic later in this article. I have a particular job running (Hive query) which has two input datasets - one a very large, partitioned dataset, the other a small (~250 rows, 2 columns), non-partitioned dataset. The EXPLAIN QUERY PLAN Command. Resting is a buff status effect which restores player health and prevents the depletion of player hunger. What is Hive? Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. One example, per Eli Collins: Facebook has a 500 petabyte Hive warehouse, but jokes that on a good day an analyst can run 6 queries against it. Computer Running Slow Fix : Get Rid of PC Issues in 3 Easy Steps with Guaranteed Results ★ [ COMPUTER RUNNING SLOW FIX ] ★ Free Diagnose Your Computer For Errors. Codecademy is the easiest way to learn how to code. I have my exercise program ready and I am currently doing it 5 days per week (hour session each time). 203e and Spark 2. This video shows how to run live analytics using Tableau against Apache Hive LLAP on AWS. Here is an example of a Hortonworks Hadoop Hive data source using Tableau Desktop on a Windows. A Hive interactive query that runs on the Hortonworks Data Platform (HDP) meets low-latency, variably guaged benchmarks to which Hive LLAP responds in 15 seconds or fewer. That’s not really a query where you need window functions. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. Mechanism: 1. Glossary of commonly used SQL commands. com for more updates on big data and other technologies. Avoid Exceeding Throughput. There are several projects trying to reduces this problem like TEZ from the stinger. For example, suppose that your data is located at the following S3 paths:. Migrate from Hive From the course: Presto Essentials: and some of the differences that you'll likely run into if you are migrating away from using Hive for your analysis query language into. Spark, Hive, Impala and Presto are SQL based engines. However, in last few months I heard quite a bit of complaints from customers attempting to query Azure tables of performance. Thanks to its Hive compatibility, Shark can query data in any system that supports the Hadoop storage API, including HDFS and Amazon S3. The author of the query does not need to worry about the underlying implementation - Hive handles this automatically. Hive Query running super slow in 5. Hive Web Interface (HWI) is a simple graphical user interface (GUI) of the Hive. Write support provides an alternative way to run big queries by breaking them into smaller queries. We ask for a question, then wait for an answer. 4) to install the 32-bit Oracle client, or to 64-bit ODAC 12c Release 4 (12. You can use the Hive Query executor with any event-generating stage where the logic suits your needs. In this case, Hive will return the results by performing an HDFS operation (hadoop fs -get equivalent). Even if there is an index on the appointment_date column in the table users, the query will still need to perform a full table scan. The cost-based optimizer (CBO) tries to generate the most efficient join order. A view would mask the complexity of the schema to the end users by only providing one table with custom and dedicated ACLs. No login scripts. See the Tableau Knowledge Base for detailed instructions on connecting Hive data to Tableau. I'm not sure what the problem is, but seems to be a Hive performance issue when it comes to "highly partitioned" tables. Without partitioning Hive reads all the data in the directory and applies the query filters on it. The EXPLAIN QUERY PLAN Command. For more information, see HiveServer2 Overview on the Apache Hive website. Then you will get the main reason. When compared to the performance achieved by traditional relation database queries, Hive's response times are often unacceptably slow and often leave you wondering how you can achieve the type of performance your end users are accustomed to. Efficient processing of Top-k queries is a crucial requirement in many interactive environments that involve massive amounts of data. just use impala. Hadoop Tutorials: Ingesting XML in Hive using XPath Author Intel Business Published on August 15, 2013 In the first of my series of Hadoop tutorials, I wanted to share an interesting case that arose when I was experiencing poor performance trying to do queries and computations on a set of XML Data. com # next two steps direct hive to use the just. Using MySQL as a Hive backend database Hive let us use a SQL like (HiveQL) style to analyse large datasets with ad-hoc queries, and comes as a service on top of hdfs. Hive Performance - 10 Best Practices for Apache Hive June 26, 2014 by Nate Philip Updated July 13th, 2018 Apache Hive is an SQL-like software used with Hadoop to give users the capability of performing SQL-like queries on it's own language, HiveQL, quickly and efficiently. 0 recommended. The help desk or database team usually hears that described as the application is slow or the database is slow. This includes Apache YARN for batch processing, and Apache Tez for more ad-hoc type of queries. Bowen Hive are pleased to announce the very talented Robin Dudley-Howes, jewelry designer extradonaire will be arriving on Bowen Island on the 25th, 26th and 27th of March 2011 to run three of her sell out courses just for you guys!!! Space is limited, so please book early to avoid disappointment. While Apache Hive writes data to a temporary location and move them to S3. Here are some tips on how to manage their resource. Use the Hive Query executor in an event stream. The script you provided does show an improvement in IO and CPU time, but you are comparing apples and oranges here. Other query systems within Facebook, such as Hive [20] and Peregrine [13], query data that is written to HDFS with a long (typ-ically one day) latency before data is made available to queries and queries themselves take. In this case, Hive will return the results by performing an HDFS operation (hadoop fs -get equivalent). ALTER TABLE ADD PARTITION. For query plan optimization to work correctly, make sure that the columns that are involved in joins, filters, and aggregates have column statistics and that hive. By enabling compression at various phases (i. With a fetch task, Hive directly goes to the file and gives the result, rather than start a MapReduce job for the incoming query. Hive gives a SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop. Computer Running Slow Fix. SQL Server internally tries to automatically turn simple non-parameterized user queries into parameterized queries to take advantage of this performance gain. Join Ben Sullins for an in-depth discussion in this video, Why use Hive, part of Analyzing Big Data with Hive. Create a Job to Load Hive. This book contains Apache Hive Technical interview questions that you can expect in a Technical interview. I've also been looking at jstack and not sure why it's so slow. Create a Job to Load Hive. 10, hbase 0. Lower values might lead to longer execution times as more jobs will be run. Attach hive job screenshot. how the data is distributed across the spus, etc There are different ways to check how your netezza box is performing. How to monitor Netezza performance? Performance of Netezza depends on various factors such as distribution keys on the table, query performance, hardware factors such as number of spus, data skew i. How To Fix A Slow Computer Once you've determined that you carry rid yourself of all unnecessary files, go online. It was designed by Facebook people. Step 5: Run the Hive metastore process so that when Spark SQL runs, it can connect to metastore uris and take from it the hive-site. Well, let's imagine that you made sure, that everything that may work on the cell side works there (in other words you don't have a lot of "External Procedure Call" wait events), don't have any Oracle Database related problem, Storage Indexes warmed up, but you may still think that query. If a user poses a query that cannot be answered by the local database alone, ANGIE calls the appropriate. In this task you will be creating a job to load parsed and delimited weblog data into a Hive table. Conclusion. When I use a database management system to query Hive -- like DBeaver, for instance -- I can get around this by running the queries through the Tez engine, with the statement below:. Note: all. - Your TalkTalk Phone or mobile number - Your Signature TalkTalk Correspondence Dept PO Box 675 Salford M5 0NL. If the user knows in advance that the inputs are small enough to fit in memory, the following configurable parameters can be used to make sure that the query executes in a single map-reduce job. This SQL tutorial explains how to use the AND condition and the OR condition together in a single query with syntax and examples. 2010/10/01 hive query doesn't seem to limit itself to partitions based on the WHERE clause Marc Limotte 2010/10/01 Re: wrong number of records loaded to a table is returned by Hive gaurav jain 2010/10/01 Re: dynamic partition query dies with LeaseExpiredException Dave Brondsema. Tip 1: Partitioning Hive Tables Hive is a powerful tool to perform queries on large data sets and it is particularly good at queries that require full table scans. All the above functions are present in Apache Hive 0. Then take action accordingly. Many Hadoop users get confused when it comes to the selection of these for managing database. So I was able to get Hadoop 2. Because we're kicking off a map-reduce job to query the data and because the data is being pulled out of S3 to our local machine, it's a bit slow. Using traditional approach, it make expensive to process large set of data. 0 on Tez is fast enough to outperform Presto 0. Instead, the Spark application would be kept running and used by subsequent queries submitted in the same Hive session, until the session is closed. It provides a simple SQL-like language called Hive Query Language (HQL) for querying and analyzing the data stored in Hadoop clusters. 2) to read data from hive tables. PHI-BLAST performs the search but limits alignments to those that match a pattern in the query.