Hive Query Running Slow

Following query can be used to retrieve data from precipitation_data. Reports based on Hadoop-Hive are not suitable for dashboards. Query processing speed in Hive is slow but Impala is 6-69 times faster than Hive. Suppose the following table as the input. 203e and SparkSQL 2. The process of doing contains the following steps:. Presto vs Hive on MR3. 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. Keep your storage accounts and metastore database together as a unit in your application. Hive treats missing values through a special value NULL as indicated here. …It just gives you a more robust. I cant change the query. If the data is bucketted in hive, you may use hive. In this case, Hive will return the results by performing an HDFS operation (hadoop fs -get equivalent). Using Spark SQL to query data. It also uses standard ANSI SQL, which Kramolisch said is easier to learn than the Hive Query Language and its “lots of hidden gotchas. Indexes are made on top of tables so that they speed up queries. From Hive to Impala. Hive also has a great support for different file formats (given the appropriate SerDe is configured for the table). make table and even select and it is till slow. Spark, Hive, Impala and Presto are SQL based engines. Results: As outlined in the above results, Interactive Query is a super optimized engine for running concurrent queries. But pls be aware that impala will use more memory. Troubleshoot: Open beeline and verify the value of set hive. In this tutorial, you learn how to create a dataframe from a csv file, and how to run interactive Spark SQL queries against an Apache Spark cluster in Azure HDInsight. SELECT * FROM precipitation_data; Indexing. commit phase has been running for almost 16 hours and has not finished yet. A few, sometimes just one, of the reducers seem to run for much longer than the others. To do so, you should: 1. After the data is loaded, the query select * from should return data. noconditionaltask - Whether Hive enable the optimization about converting common join into mapjoin based on the input file size. Third, you can partition tables. 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). On Windows, use rename rather than mv. One of the most common problems when running SQL Servers is slow queries. What is Hive? Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. query run from SAS batch to a HIVE partitioned table takes half an hour Solved Well, I found out the select distinct query is slow in Beeline too. We all know that hive is a query language which is similar to sql built on hadoop eco-system to run queries on petabytes of data. Windows Registry Hive File A slow computer might be caused by too much data on your hard drive and a fragmented disk. But planning the query can take as long as running it. The help desk or database team usually hears that described as the application is slow or the database is slow. Owen O'Malley gave a talk at Hadoop Summit EU 2013 about optimizing Hive queries. It also uses standard ANSI SQL, which Kramolisch said is easier to learn than the Hive Query Language and its “lots of hidden gotchas. I'm not sure what the problem is, but seems to be a Hive performance issue when it comes to "highly partitioned" tables. Results: As outlined in the above results, Interactive Query is a super optimized engine for running concurrent queries. An alternative to running ‘show tables’ or ‘show. 94, hadoop 1. (Refer to Software Preparation in Cloudera -Hive Setup) HDP: Hive 1. Starting with Hive 1. 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. Impala is meant to be good at what Hive is bad at – i. One of the most common problems when running SQL Servers is slow queries. Hive can read text files like logs, CSV, or JSON format data exported from other systems and Hive output as well can be in text format. Using Hive for Analytical Queries Hi, and welcome to this course on Writing Complex Analytical Queries with Hive. Spark SQL reuses the Hive frontend and MetaStore. Second, column-oriented storage options can be quite helpful. > > Hive queries run in many minutes. This information is used to find data so the distributed resources can be used to respond to queries. On the whole, Hive on MR3 is more mature than Impala in that it can handle a more diverse range of queries. BlastP simply compares a protein query to a protein database. The first run will be slow, but after few times query will be finished within couple seconds. > Hive will process all data in the CF (brute force), possibly multiple times. bucketmapjoin or hive. A Tez ApplicationMaster (AM) monitors the query while it is running. Hadoop queries in Pig or Hive can be too slow for real-time data analysis. If you have access to a server with SQL*Plus, you can run the query there in the background. So far we have seen running Spark SQL queries on RDDs. The query has been running for several hours and is still not finished. 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. You can find these free programs by running an easy query on favorite search engine. As a data scientist working with Hadoop, I often use Apache Hive to explore data, make ad-hoc queries or build data pipelines. Hadoop was built to organize and store massive amounts of data of all shapes, sizes and formats. To apply the partitioning in hive, users need to understand the domain of the data on which they are doing analysis. The query you posted is the exact exception I stated earlier. What if we want to process the data by some ETL programs, and load the result data to hive, but we don’t want to load them manually? What if the data is not only used by hive, but also some other applications, maybe it still need to be MapReduced? External table comes to save us by creating table using following syntax,. Its takes more than 4 hours to complete. With over 100 petabytes of data in HDFS, 100,000 vcores in our compute cluster, 100,000 Presto queries per day, 10,000 Spark jobs per day, and 20,000 Hive queries per day, our Hadoop analytics architecture was hitting scalability limitations and many services were affected by high data latency. Whether it is for OS time, Network time , Buffer time or other. 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. 11/2/2017; 3 minutes to read +1; In this article. The SQL AND condition and OR condition can be combined to test for multiple conditions in a SELECT, INSERT, UPDATE, or DELETE statement. Learn 5 ways to make your Apache Hive queries run faster on your Hadoop cluster. 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. As long as the queries would have really returned the same plan, this is a big performance winner. 05/16/2019; 3 minutes to read +3; In this article. Comparison of Hive's query optimisation techniques. Running HiveQL queries using Spark SQL. I've been monitoring jmap, and don't believe it's a memory or gc issue. A Hive join query takes an inordinately long time, and the console output shows "Reduce=99%" for much of the total execution time. By 2011, that solution became too rigid and slow. Configure Hive Connector properties for Generated SQL. These issues cause the Ambari web interface to show an alert for the Hive metastore even though the process is running. Be in control Everything is at your fingertips. Monitor your rigs from a single dashboard. : Select, update and insert operation. But you can also run Hive queries using Spark SQL. Need some configuration to install. You may notice that for queries with only a connection (not loaded locally), is that there is no Table Tools contextual ribbon tabs available for the Query – but these features can all be accessed by right-clicking on the Query in the Workbook Queries pane. method identifier in here to see where the slow query is coming from. In addition, the Processes tab of the Windows Task Manager might indicate that the tabprotosrv. This can happen due to a variety of reasons. This blog explains how to load the registry hive file NTUSER. Our Hive extension each_top_k helps running Top-k processing efficiently. After a few queries in Hive, I started to find Hadoop slow compared to my expectation (and slow compared to Netezza). enable is enabled. Go to SQL Server Management Studio (SSMS) and connect to an SQL instance. If you are interested in Hive LLAP Interactive query, Scheduler Run your jobs on simple or Run you Hive LLAP & PySpark Job in Visual Studio Code. You can use the Hive Query executor with any event-generating stage where the logic suits your needs. The following article demonstrates how unstructured data and relational data can be queried, joined and processed in a single query using PolyBase, a new feature in SQL Server 2016. 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. Without partitioning, Hive reads all the data in the directory and applies the query filters on it. So, directly writing the INSERT OVERWRITE query results to S3 is an optimization that Qubole Hive offers you. g "select session_id from app_sessions_prod where 1=1 and session_id = '8043472_2015-05-07 06:55:24' limit 5;" then it is running very slow. com for more updates on big data and other technologies. Home Big Data Hive query failed with error: Killing the Job. Data types. Earlier when i fire the same query it took around 5 minutes and now it is taking around 22 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. 1, queries executed against table 'default. Test 7: Run all 99 queries, 64 at a time - Concurrency = 64. In addition, the Processes tab of the Windows Task Manager might indicate that the tabprotosrv. Most popular column that are used very often in WHERE clause should be indexed to make the query run faster. Review a third table called recommendation. The only limit to the size of the queries, groups, and sorting is the disk capacity of the cluster. The hive loading stage is not only "moving" file in hdfs from the data/ dir into the hive/warehouse. In non-strict mode, all partitions are allowed to be dynamic. Speed up your Hive queries. 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. Hive, Spark ) Ability to run ANSI SQL based queries against. This protects you from SQL injection attacks, and as an added benefit, the database can often optimise the query so it runs faster. LLAP enables application development and IT infrastructure to run queries that return real-time. An alternative to running ‘show tables’ or ‘show. SQL Server internally tries to automatically turn simple non-parameterized user queries into parameterized queries to take advantage of this performance gain. QuerySurge Database Backup Procedures QuerySurge is backed by a MySQL database. select count(*) from foo limit 1 uses mapreduce and takes over a minute. As a data scientist working with Hadoop, I often use Apache Hive to explore data, make ad-hoc queries or build data pipelines. > > -Jake >. hive-staging", which will be placed under the target directory when running "INSERT OVERWRITE" query, Hive will grab all files under the staging directory and copy them ONE BY ONE to target directory. A slow running Hive query is usually a sign of sub-optimal configuration. If you want to run serious JDBC applications, i. (10 replies) Cluster Information: Total of 5 Nodes in the cluster - with CDH42 installed by RPM and impala beta. The Hive query execution engine converted this query into MapReduce jobs. Hive is slow, and I'd use it only if we cannot use something like Presto/Impala. Windows Registry Hive File A slow computer might be caused by too much data on your hard drive and a fragmented disk. With a fetch task, Hive directly goes to the file and gives the result, rather than start a MapReduce job for the incoming query. Speed up your Hive queries. Efficient processing of Top-k queries is a crucial requirement in many interactive environments that involve massive amounts of data. Similarly Hive on Tez in HDP 3. You can still edit, merge or append queries as normal while load is disabled. 94, hadoop 1. With its SQL-like interface, Hive is extensively used by analysts to extract insights from big data. Facebook this week contributed Presto, its new in-memory distributed query engine that is up to 10 times faster than Hive, into the open source realm. All modern database engines provide a way to write parameterised queries, queries that contain some placeholder that allows you to re-run the query multiple times with different inputs. Note: Before you can connect to an Oracle database using Power Query, you need the Oracle client software v8. A cached search is deleted after 60 seconds but after months of deleting searches without using the optimize function of MySQL the table was 900 MB big which is a lot for a table containing 100 rows at peak times. PDF | The size of data has been growing day by day in rapidly way. • Join Optimization: Shark uses PDE to select the join strategy at runtime. Driver class. xml with the hive. but are very slow. 0 is the slowest on both clusters not because some queries fail with a timeout, but because almost all queries just run slow. Overdrive Staff “You can’t stop for more than a few minutes during a warm day’s run,” says a fellow North Dakota owner-operator, Lee Eberts, who has been hauling bees. We hear these buzzwords all the time, but what do they actually mean? In this post, I’ll walk through the basics of Hadoop, MapReduce, and Hive through a simple example. I've been monitoring jmap, and don't believe it's a memory or gc issue. To install the Oracle client software, go to 32-bit Oracle Data Access Components (ODAC) with Oracle Developer Tools for Visual Studio (12. 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. Some more configurations need to be done after the successful. Reverse engineering from Hive database processing is slow due to the absence of system tables. The Hive Query Language is a subset of SQL-92. 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. ” 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. 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. Concretely, we take the sum of sales in the second table over every row that has a date less than or equal to the date coming from the first table. The Hive metastore process shows high CPU usage, or there is slow start-up time of the Hive command line interface (CLI) in a ppc64 environment. Attunity Compose for Hive automates the data pipeline to create analytics-ready data by leveraging the latest innovations in Hadoop such as. Partition swapping in Hive. exe process is using an extremely large amount of memory. This includes Apache YARN for batch processing, and Apache Tez for more ad-hoc type of queries. Its takes more than 4 hours to complete. Be in control Everything is at your fingertips. If you want to run serious JDBC applications, i. So we built JSON file for each segment from geo­map visualization. 0 is the slowest on both clusters not because some queries fail with a timeout, but because almost all queries just run slow. We all know that hive is a query language which is similar to sql built on hadoop eco-system to run queries on petabytes of data. The commands in SQL are called Queries and they are of two types. For more on how to configure this feature, please refer to the Hive Tables section. Spark, Hive, Impala and Presto are SQL based engines. Hadoop-Hive data sources are not suitable for creating reports interactively in the Ad Hoc Editor. One thing that Intelligence was able to discover however, was the frequency of which the controllers of this hive mind exerted their influence with. Partitioning Tables Hive partitioning is an effective method to improve the query performance on larger tables. This SQL tutorial explains how to use the AND condition and the OR condition together in a single query with syntax and examples. Conclusion. Running those queries using Hive is an option but those skewed JOIN queries are even slower on Hive. why? i shouldn't have to analyze simple queries like this to find workarounds that make them reasonably performant. Running SQL Queries in a Loop. Is there any way to get to linear distance using the Hive ST_* functions in an SQL query?. Hortonworks Hadoop Hive data source example. 0 each INSERT INTO T can take a column list like INSERT INTO T (z, x, c1). The SQL AND condition and OR condition can be combined to test for multiple conditions in a SELECT, INSERT, UPDATE, or DELETE statement. Pig vs Hive: Benchmarking High Level Query Languages Benjamin Jakobus IBM, Ireland Dr. If an application is Hive-aware, the Hortonworks Hive ODBC Driver is configurable to pass the query through. August 9, 2016. enable is enabled. Close the Hive Shell: You are done with the Hive Shell for now, so close it by entering 'quit;' in the Hive Shell. This is slow and expensive since all data has to be read. As a data scientist working with Hadoop, I often use Apache Hive to explore data, make ad-hoc queries or build data pipelines. Where, Hiveql Select Order By, Hiveql Group By, Hiveql Joins, Built-in functions, Views and Indexes. 5s on my box, as opposed to 6. The first part of the article is based on the talk What's new in Apache Hive given by Jason Dere at the DataWorks Summit 2019 Barcelona. , with multiple concurrent users, with complex queries and on large datasets, we recommend you increase the memory and CPU allocation. Tools like Impala and Hawq provide interfaces that enable end users to write queries in the SQL programming language. Data Definition Query: The statements which defines the structure of a database, create tables, specify their keys, indexes and so on; Data manipulation queries: These are the queries which can be edited. 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. If the data is bucketted in hive, you may use hive. By 2011, that solution became too rigid and slow. HiveServer2 allows clients such as Beeline or SQL Workbench/J to run queries against Hive. One of the biggest challenges Hive users face is the slow response time experienced by end users who are running ad hoc queries. stop the Spark ThriftServer from the Ambari console. Slow changing dimensions. Need some configuration to install. In Mapreduce processing, Huge number of partitions will lead to huge no of tasks (which will run in separate JVM) in each mapreduce job, thus creates lot of overhead in maintaining JVM start up and tear down. This is Postgres. Some users simultaneously refresh hundreds of queries on a dashboard multiple times every day, while others run individual queries on an occasional ad-hoc basis throughout their workday. I've also been looking at jstack and not sure why it's so slow. Enable Compression in Hive. For simple queries like SELECT * with limit, it is much faster. And all other variables containing "$" within sqoop command are escaped by single quoting the variable itself like the value of "--target-dir". Windows Registry Hive Location You can fix slow computer up by deleting the unneeded files because of your hard dr. Apache Hive has been an important part of that promise. 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). What is Hive? Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. This is a big deal for big data, because with Impala, querying. 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. You may notice that for queries with only a connection (not loaded locally), is that there is no Table Tools contextual ribbon tabs available for the Query – but these features can all be accessed by right-clicking on the Query in the Workbook Queries pane. Keep visiting our site www. This post describes the problem of small ORC and Parquet files in HDFS and how it affects Big SQL read performance. So iI thought I should share the params that improved the performance of the query. Speculative execution is a common approach to solve this problem by backing up slow tasks on alternate. …And again remember Presto can work with Hive…in fact it kind of is built in…and so it works really well. Read this hive tutorial to learn Hive Query Language - HIVEQL, how it can be extended to improve query performance and bucketing in Hive. For example, if you run a Snowflake X-Small warehouse for one hour at $2/hour, and during that time you run one query that takes 30 minutes, that query cost you $2 and your warehouse was idle 50% of the time. Go to SQL Server Management Studio (SSMS) and connect to an SQL instance. 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?. bucketmapjoin or hive. 1, queries executed against table 'default. How to Improve Hive Query Performance With Hadoop Apache Hive is a powerful tool for analyzing data. Sqlplus run in background with nohup Sometimes you have to run a query that takes FOREVER, and you want to go home with your laptop. The QuerySurge database persists all your QuerySurge data, including QueryPairs, Suites, Scenarios and Results data. One thing that Intelligence was able to discover however, was the frequency of which the controllers of this hive mind exerted their influence with. Hive is bad at ad-hoc query, unless you really, really need Hive’s scale and low license cost. An Introduction to SQL on Hadoop and SQL off Hadoop There is more detail on how the benchmark was run, and the per-query results here. And start the custom spark-thrift server as below. how the data is distributed across the spus, etc There are different ways to check how your netezza box is performing. output property to true. This video shows how to run live analytics using Tableau against Apache Hive LLAP on AWS. This example data set demonstrates Hive query language optimization. Schema-RDDs provide a single interface for efficiently working with structured data, including Apache Hive tables, parquet files and JSON files. Analytic query engine compatible with Hive – Supports Hive QL, UDFs, SerDes, scripts, types – A few esoteric features not yet supported Makes Hive queries run much faster – Builds on top of Spark, a fast compute engine – Allows (optionally) caching data in a cluster’s memory – Various other performance optimizations. Troubleshoot Apache Hive by using Azure HDInsight. Don't worry about using a different engine for historical data. 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. The query has been running for several hours and is still not finished. PDF | The size of data has been growing day by day in rapidly way. It shows the history of all Hive queries executed on the cluster whether run from Hive View or another source such as JDBC/ODBC or CLI. We need to get the data refining (aka ETL) phase up and going for that first. Partitions may optimize some queries based on Where clauses, but may be less responsive for other important queries on grouping clauses. QuerySurge Database Backup Procedures QuerySurge is backed by a MySQL database. slow to query• Often best to denormalize during load – Write once, read many. 203e and SparkSQL 2. Hive "loading"-stage is slow. When using Athena with the AWS Glue Data Catalog, you can use AWS Glue to create databases and tables (schema) to be queried in Athena, or you can use Athena to create schema and then use them in AWS Glue and related services. processing (LLAP) can improve the performance of interactive queries. For more advanced stats collection need to run analyze table queries. noconditionaltask - Whether Hive enable the optimization about converting common join into mapjoin based on the input file size. You can optimize Hive queries in at least five ways: First, with a little research, you can often speed your joins by leveraging certain optimization techniques, as described on the Hive wiki. A self join is a query that compares a table to itself. Important When enabling Hive LLAP, the Run as end user instead of Hive user slider on the Settings tab has no effect on the Hive instance. Without partitioning Hive reads all the data in the directory and applies the query filters on it. Hive Query Running Slow. How To Fix A Slow Computer Once you've determined that you carry rid yourself of all unnecessary files, go online. We need to get the data refining (aka ETL) phase up and going for that first. Apache Drill is more like Presto. Multi Table Inserts minimize the number of data scans required. Hive is bad at ad-hoc query, unless you really, really need Hive’s scale and low license cost. A common mis-perception is Hadoop and Hive are slow, but with the introduction of Hive LLAP and various. Facebook this week contributed Presto, its new in-memory distributed query engine that is up to 10 times faster than Hive, into the open source realm. 0, the HBase Hive integration only supported querying primitive data types in columns. 4) to install the 32-bit Oracle client, or to 64-bit ODAC 12c Release 4 (12. One of the queries is: select a. In nearly all parts, we have coded MapReduce jobs to solve specific types of queries (filtering, aggregation, sorting, joining, etc…). slow queries on apache drill comparing drill and hive queries to see if we want to go forward with leveraging MapR Drill for ad-hoc queries. Very often users need to filter the data on specific column values. Still if you need quick result, you have to login to impala-shell instead of Hive and run your query. Complex query can be tuned but applying count(*) query on hive table with 4 million records returning result in 15 seconds is not an issue from Hive point of view. Hive Compatibility − Run unmodified Hive queries on existing warehouses. Hive translate your query into temporary Map/Reduce job and that job executed on behalf of your hive query. Then you will get the main reason. Hive gives a SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop. bucketmapjoin. Then you can use readLines() and separate out fields based on the '\t' delimiter, creating a data. A Hive join query takes an inordinately long time, and the console output shows "Reduce=99%" for much of the total execution time. Speed up your Hive queries. You can write your code in dplyr syntax, and dplyr will translate your code into SQL. Hive, Spark ) Ability to run ANSI SQL based queries against. txt Run queries in Cron. 10, hbase 0. This means your pc will run so slow it are hard to obtain anything over. 4) Xcopy for Windows x64 to. This is because we use the DATEDIFF function on the column appointment_date. Hive and Impala implement different, disjointed subsets of what Apache Drill is capable of. 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. Linguistic Data Consortium corpora available via the SALTS Lab; Rutgers Digital Humanities Initiative. 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. In this instructional post, we will see how to run Hive queries using the Hive Web Interface (HWI). Each Hive query is translated to at least one. If you have access to a server with SQL*Plus, you can run the query there in the background. Partition swapping in Hive. A slow running Hive query is usually a sign of sub-optimal configuration. Hive can insert data into multiple tables by scanning the input data just once (and applying different query operators) to the input data. Developed by Facebook for internal assignments, Hive has quickly gained traction and has become a top choice for running queries on Hadoop for experienced SQL practitioners. New features and changes are introduced for IBM InfoSphere Information Server, Version 11. Speed up your Hive queries. 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. A common mis-perception is Hadoop and Hive are slow, but with the introduction of Hive LLAP and various. why? i shouldn't have to analyze simple queries like this to find workarounds that make them reasonably performant. 3 Benefits of Apache Hive View 2. However, to run queries on petabytes of data we all know that hive is a query language which is similar to SQL built on Hadoop ecosystem. 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. provides Hadoop management as well as access to Hive and HDFS • Hue - is an open source development by Cloudera. A data scientist’s perspective. 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. In particular, it achieves a reduction of about 25% in the total running time when compared with Hive 3. Elastic Map Reduce allows you to conveniently run SQL-like queries against DynamoDB using Hive. …It just gives you a more robust. It's worth noting that in MapR, an easier way to handle this use case is to take a snapshot and run your read queries on that snapshot. remove setting might be an option to consider. Earlier when i fire the same query it took around 5 minutes and now it is taking around 22 minutes. Spark SQL allows you to execute Spark queries using a variation of the SQL language.