The SQL UNION Operator. The hive query which is used by my batch is taking too much time to run. 0 Apache Hive 2. 0 onward supports storing and querying Avro objects in HBase columns by making them visible as structs to Hive. Cloudera provides the world's fastest, easiest, and most secure Hadoop platform. The alternative to the above problem would be to distribute our database load on multiple hosts as the load increases. For those interested the problem lies in a database table containing cached searches for the resource sections. make table and even select and it is till slow. If your distribution does not support Hive on Spark, your options are either Tez or MapReduce. Warning: The data returned by the EXPLAIN QUERY PLAN command is intended for interactive debugging only. Third, you can partition tables. In Hive Latency is high but in Impala Latency is low. If Hive integration is enabled, we need to specify the schema. In queries using set operators, Oracle does not perform implicit conversion across datatype groups. stats: When set to true Hive will answer a few queries like count(1) purely using stats stored in metastore. Click Service Actions > Restart All. Phoenix achieves as good or likely better performance than if you hand-coded it yourself (not to mention with a heck of a lot less code) by: compiling your SQL queries to native HBase scans; determining the optimal start and stop for your scan key. 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. For long-running queries, Hive on MR3 runs slightly faster than Impala. Hive Performance Tuning: Below are the list of practices that we can follow to optimize Hive Queries. Hadoop was built to organize and store massive amounts of data of all shapes, sizes and formats. Hive "loading"-stage is slow. 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. Efficient processing of Top-k queries is a crucial requirement in many interactive environments that involve massive amounts of data. If all queries select values of type NUMBER, then the returned values have datatype NUMBER. If our data is not inside MySQL you can’t use “sql” to query it. how the data is distributed across the spus, etc There are different ways to check how your netezza box is performing. The Hive Query executor is designed to run a set of Hive or Impala queries after receiving an event record. 4) Xcopy for Windows x64 to. 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. If you are using JDBC to connect to Hive and you issue concurrent queries using a single. Query processing speed in Hive is slow but Impala is 6-69 times faster than Hive. 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:. Apache Hive performance monitoring from DRIVEN monitors your HQL queries across all your Hadoop clusters for better big data management. I'm trying to optimize the query by enforcing map join as mentioned here When i enforce the parameters mentioned in your blog as mentioned, My run time is going higher than actual existing query. This video shows how to run live analytics using Tableau against Apache Hive LLAP on AWS. 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. Using traditional approach, it make expensive to process large set of data. Test 5: Run all 99 queries, 16 at a time - Concurrency = 16. The process of doing contains the following steps:. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. Slow or stalled queries under highly concurrent write workloads when Sentry + Hive are used, caused by a Hive metastore deadlock In the affected releases, some workloads can cause a deadlock in the Hive metastore. Since MapR snapshots are guaranteed to be consistent, your read queries on a snapshot will see a completely static view of your Hive tables. After learning Apache Hive, try your hands on Latest Free Hive Quiz and get to know your learning so far. Spark Dataframes: All you need to know to rewrite your Hive/Pig scripts to spark DF In this blog post, I am going to talk about how Spark DataFrames can potentially replace hive/pig in big data space. Use the Hive Query executor in an event stream. In this tutorial we learned how to import an existing CSV file into Elasticsearch with Logstash to perform later analysis with Hadoop's Hive. 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. It can be solved easily with SQL-92 aggregate functions, which run on any database: SELECT movie_name FROM my_table GROUP BY movie_name ORDER BY count(*) DESC LIMIT 3 -- I'm assuming you're using MySQL or PostgreSQL here Note that a great platform to ask these questions is Stack Overflow. For those interested the problem lies in a database table containing cached searches for the resource sections. 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. From Hive to Impala. Spark SQL reuses the Hive frontend and MetaStore. It provides a simple SQL-like language called Hive Query Language (HQL) for querying and analyzing the data stored in Hadoop clusters. how the data is distributed across the spus, etc There are different ways to check how your netezza box is performing. Hive can insert data into multiple tables by scanning the input data just once (and applying different query operators) to the input data. A Hive join query takes an inordinately long time, and the console output shows "Reduce=99%" for much of the total execution time. In S3, moving data is expensive (involves copy and delete operations). 1 is fast enough to outperform Presto 0. In this case, we’re comparing each date to any date less than or equal to it in order to calculate the running total. 2, we continue to improve the end user query experience with Hue, focusing on easier SQL query troubleshooting and increased compatibility with Hive. (4 replies) Hello All, I am trying to use the ODBC driver but making ODBC Calls to fetch a list of tables from Hive is extremely slow on a HiveServer2. Partitioning allows you to store data in separate sub-directories under table location. Close the Hive Shell: You are done with the Hive Shell for now, so close it by entering 'quit;' in the Hive Shell. 2) to read data from hive tables. stop the Spark ThriftServer from the Ambari console. Windows Registry Hive File A slow computer might be caused by too much data on your hard drive and a fragmented disk. The editor is used a lot for querying Hive and Impala. Thanks to its Hive compatibility, Shark can query data in any system that supports the Hadoop storage API, including HDFS and Amazon S3. Reverse engineering from Hive database processing is slow due to the absence of system tables. Second, column-oriented storage options can be quite helpful. Hadoop was built to organize and store massive amounts of data of all shapes, sizes and formats. That's great news. Hue uses same session to run user queries and background refresh queries. 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. How can I tell WHY an insert on a certain table is slow? Ask Question That's unacceptably slow, as it causes other spids to time out. And they will query Hive (via pyhs2) and Postgres (via pycopg2) respectively, and return the result in JSON format. To apply the partitioning in hive, users need to understand the domain of the data on which they are doing analysis. Starting with Hive 1. Hive converts SQL to Hadoop MR jobs. Use the comparison to determine whether metadata caching will be useful. 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. Impala is developed and shipped by Cloudera. 1, you can create a Hive view that facilitates writing and running Hive queries quite easily. Uberized Tasks – Make MapReduce More Interactive Posted on January 26, 2015 by admin Compared with batch processing, interactive processing assumes that you get response to your queries within a few seconds or at least a few dozens of seconds. This information is used to find data so the distributed resources can be used to respond to 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. 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. The SQL UNION Operator. The datastage job includes a Hive Connector stage that specifies details about accessing Hive and a sequential file stage where data extracted to. Many Hadoop users get confused when it comes to the selection of these for managing database. LLAP is optimized for queries that involve joins and aggregates. The only limit to the size of the queries, groups, and sorting is the disk capacity of the cluster. In nearly all parts, we have coded MapReduce jobs to solve specific types of queries (filtering, aggregation, sorting, joining, etc…). If the data is bucketted in hive, you may use hive. 4 installed on both machines, got hdfs, yarn, hive etc running successfully. The required information is retrieved by manual parsing methods instead of a query language. : Select, update and insert operation. To avoid this latency, Impala avoids Map Reduce and access the data directly using specialized distributed query engine similar to RDBMS. The problem is that the query performance is really slow (hive 0. 12 supported syntax for 7/10 queries, running between 91. To do this, please run below commands before the query:. I'm using Amazon EMR to run Apache Hive queries against an Amazon DynamoDB table. Glossary of commonly used SQL commands. The result is that any query that does not generate lineage will clear the lineage for any other queries that are running in parallel. It's a bit of an odd (and slow) example (esp on my small VM set up / example data), since in pure ES you'd just run a faceted open query on title - but it shows that we can talk to ES using Hive SQL. noconditionaltask - Whether Hive enable the optimization about converting common join into mapjoin based on the input file size. Amazon Redshift is 10x faster and cheaper than Hadoop and Hive It is still running MR queries beneath it. It's very important that you know how to improve the performance of query when you are. provides Hadoop management as well as access to Hive and HDFS • Hue - is an open source development by Cloudera. Use custom SQL to connect to a specific query rather than the entire data source. SQL Server internally tries to automatically turn simple non-parameterized user queries into parameterized queries to take advantage of this performance gain. 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. Click the Save button near the top of the Ambari window. This information is used to find data so the distributed resources can be used to respond to queries. 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. Download an adware and malware removal program. 0 Apache Hive 2. These tables can be queried using pycopg2 library. Spark SQL query execution is very very slow when comparing to hive query execution Question by Govinda Rao Peddakota May 16, 2016 at 01:59 PM spark-sql Hi, I am using Spark Sql(ver 1. old shell> mv mysql-slow. The Hortonworks Hive ODBC Driver with SQL Connector interrogates Hive to obtain schema information to present to a SQL-based application. It could not keep up with the growing data ingestion and query rates. For query: "select session_id from app_sessions_prod where 1=1 limit 5;" I'm getting the result in 15 seconds but when I'm using any where clause (including table columns) e. In this case, Hive will return the results by performing an HDFS operation (hadoop fs –get equivalent). Cloudera provides the world’s fastest, easiest, and most secure Hadoop platform. Azure PowerShell support for managing HDInsight resources using Azure Service Manager is deprecated, and was removed on January 1, 2017. (4 replies) Hello All, I am trying to use the ODBC driver but making ODBC Calls to fetch a list of tables from Hive is extremely slow on a HiveServer2. As of Hive 1. can be in the same partition or frame as the current row). I've also been looking at jstack and not sure why it's so slow. Alternatively, we can migrate the data to Parquet format. 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. Here is the performance enhancement piece. Capability to run big queries We limit queries by their runtime and the data they process on Presto. The view is getting all the records from the Hive table without any WHERE clause. Hive allows only appends, not inserts, into tables, so the INSERT keyword simply instructs Hive to append the data to the table. The editor is used a lot for querying Hive and Impala. They should be able to make changes or recommendations that will optimize the query at the database level. Reports based on Hadoop-Hive are not suitable for dashboards. 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. Avoid Exceeding Throughput. Queries in Hive LLAP are executing slower than expected. And start the custom spark-thrift server as below. Output to a file beeline -f my_query. Hive Performance Tuning: Below are the list of practices that we can follow to optimize Hive Queries. Hive uses Hadoop's Distributed Cache to distribute the added files to all the machines in the cluster at query execution time. 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. py and SQL_SELECT. 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. 12 supported syntax for 7/10 queries, running between 91. 0 each INSERT INTO T can take a column list like INSERT INTO T (z, x, c1). 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. Most popular column that are used very often in WHERE clause should be indexed to make the query run faster. If you have trouble connecting to Hive from clients using JDBC or ODBC drivers, check for errors in the hive-server2 logs:. While a Hive query is in progress, another application might load new data into the DynamoDB table or modify or delete existing data. Improving or tuning hive… August 13, 2015 By Mohammad Farooq 1. Speed up your Hive queries. Your career in Data science, Data analytics and Data warehouse can get a boost with the knowledge of Apache Hive. Without map join, my query run time is 38 seconds. 13 from which Qubole's distribution of Hive is derived (we also support Hive 1. We tried to query segment geo spatial data from hive directly for real time update but found it very slow. A self join is a query that compares a table to itself. ALTER TABLE ADD PARTITION. All the above functions are present in Apache Hive 0. Even after running it for hours. Hive was developed by the folks at Facebook in 2008, as a means of providing an easy-to-use, SQL-like. 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. PDF | The size of data has been growing day by day in rapidly way. Apache Hive performance monitoring from DRIVEN monitors your HQL queries across all your Hadoop clusters for better big data management. Spark SQL query execution is very very slow when comparing to hive query execution Question by Govinda Rao Peddakota May 16, 2016 at 01:59 PM spark-sql Hi, I am using Spark Sql(ver 1. We will see below on how we can configure Hive Connector properties of both Generated SQL and User-defined SQL. 0 onward supports storing and querying Avro objects in HBase columns by making them visible as structs to Hive. For long-running queries, Hive on MR3 runs slightly faster than Impala. You can vote up the examples you like. Earlier when i fire the same query it took around 5 minutes and now it is taking around 22 minutes. remove setting might be an option to consider. Hive "loading"-stage is slow. In addition, the Processes tab of the Windows Task Manager might indicate that the tabprotosrv. Schema-RDDs provide a single interface for efficiently working with structured data, including Apache Hive tables, parquet files and JSON files. Alternatively, we can migrate the data to Parquet format. Using Hive for Analytical Queries Hi, and welcome to this course on Writing Complex Analytical Queries with Hive. Hive only a few years ago was rare occurrence in most corporate data warehouses, but these days Hive, Spark, Tez, among others open source data warehouses are all the buzz in the corporate world and data analysts need to adapt to this changing world. 4) to install the 32-bit Oracle client, or to 64-bit ODAC 12c Release 4 (12. HIVE :-The Apache Hive ™ data warehouse software facilitates querying and managing large datasets residing in distributed storage. Multi Table Inserts minimize the number of data scans required. just use impala. Hive Web Interface (HWI) is a simple graphical user interface (GUI) of the Hive. prefix test_ if Hive is running in test mode, prefixes the output table by this string hive. Efficient Top-k Query Processing using each_top_k. Export SQL Server data to CSV by using the ApexSQL Complete Copy results as CSV option Export SQL Server data to CSV by using SQL Server export wizard. Impala is a n Existing query engine like Apache Hive has run high run time overhead, latency low throughput. A Hive join query takes an inordinately long time, and the console output shows “Reduce=99%” for much of the total execution time. (internal) Minimal increase rate in the number of partitions between attempts when executing take operator on a structured query. 3s for the join version. commit phase has been running for almost 16 hours and has not finished yet. 14 minute read. To extract it and copy the binary into a system path, run:. Hive minds where hard to get anything other than objective knowledge from, after all those who normally has the loose lips, were few and also those who controlled the rest. After a few queries in Hive, I started to find Hadoop slow compared to my expectation (and slow compared to Netezza). Yet many queries run on Hive have filtering where clauses limiting the data to be retrieved and processed, e. So, I guess it. We will see below on how we can configure Hive Connector properties of both Generated SQL and User-defined SQL. Are your looking for ways create your computer run faster? Most PC users suffer from slow running computer and don't know what to get done to improve computer success. Hive Query Running Slow. With its SQL-like interface, Hive is extensively used by analysts to extract insights from big data. Hive is bad at ad-hoc query, unless you really, really need Hive’s scale and low license cost. 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. 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. 11 supported syntax for 7/10 queries, running between 102. Move over Hive. Second, column-oriented storage options can be quite helpful. Test 7: Run all 99 queries, 64 at a time - Concurrency = 64. Yet many queries run on Hive have filtering where clauses limiting the data to be retrieved and processed, e. Write support provides an alternative way to run big queries by breaking them into smaller queries. This functionality allows Presto to run simple queries on Hadoop in just a few hundred milliseconds, with more complex queries taking only a few minutes. 13 from which Qubole’s distribution of Hive is derived (we also support Hive 1. Using Spark SQL to query data. Let's write Hive query in a file 'defaultSearchReport. 0 where queries only take a few seconds to run. Running a query similar to the following shows significant performance when a subset of rows match filter select count(c1) from t where k in (1% random k's) Following chart shows query in-memory performance of running the above query with 10M rows on 4 region servers when 1% random keys over the entire range passed in query IN clause. This SQL tutorial explains how to use the AND condition and the OR condition together in a single query with syntax and examples. Driver class. 2) to read data from hive tables. The wmf database includes filtered and preprocessed data. Then you will get the main reason. py, are create to accept SELECT statements from the request. This overcomes many of the limitations of the built-in DynamoDB query functionality and makes it significantly more useful for storing raw analytical data. It is an alternative to using the Hive command line interface. Hive Query Running Slow. 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. As of Hive 1. Although Hive creates a convenience of enabling one to run familiar SQL to SQL-like queries using Hadoop's core MapReduce component, it can get slow for extremely large datasets. This means your computer will run so slow it will be hard to obtain anything over. log and mysql-slow. Big Data maybe different, like Aster,Hive, Pig etc. Write now I am just walk/jog/running my way through a distance of 5 km twice a week. We will see below on how we can configure Hive Connector properties of both Generated SQL and User-defined SQL. 11 supported syntax for 7/10 queries, running between 102. " Presto and Impala did a little better than the other engines in terms of concurrency, or how many SQL queries can it run simultaneously. If we get that sorted out, these types of queries should be more feasible. The Netezza JDBC driver may detect a batch insert, and under the covers convert this to an external table load. There are several benefits to writing queries in dplyr syntax: you can keep the same consistent language both for R objects and database tables, no knowledge of SQL or the specific SQL variant is required, and you can take advantage of the fact that dplyr uses lazy evaluation. Query processing speed in Hive is slow but Impala is 6-69 times faster than Hive. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. See Description of HIVE-9481 for examples. 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. The process of doing contains the following steps:. When running SQL from within another programming language the results will be returned as a Dataset/DataFrame. Hive Query Running Slow. The required information is retrieved by manual parsing methods instead of a query language. On Windows, use rename rather than mv. Similarly Hive on Tez in HDP 3. log and mysql-slow. As a data scientist working with Hadoop, I often use Apache Hive to explore data, make ad-hoc queries or build data pipelines. Conclusion. The output of the function is evaluated at run time, so the server has to visit all the rows in the table to retrieve the necessary data. Part of project: Slinky Projects At work I had to do an inner join of two rather large Hive tables (~4. LLAP is optimized for queries that involve joins and aggregates. Hive also stores query logs on a per Hive session basis in /tmp//, but can be configured in hive-site. commit phase has been running for almost 16 hours and has not finished yet. To apply the partitioning in hive , users need to understand the domain of the data on which they are doing analysis. Such queries would need to join the User and Order tables with the Product table. Evaluation. By enabling compression at various phases (i. Sometimes Amazon Redshift takes hours together to just drop or truncate the tables even if table has very limited rows. Forecast Cloudy – Why Is My Azure Table Storage Query So Slow Again? Perhaps this post shouldn’t exist as I already profiled basics of Azure Table Storage in my previous post. 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. They should be able to make changes or recommendations that will optimize the query at the database level. 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. It's interactive, fun, and you can do it with your friends. Tutorial: Load data and run queries on an Apache Spark cluster in Azure HDInsight. 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. Activate the trace from ST12 for transaction and do analysis on the. This functionality allows Presto to run simple queries on Hadoop in just a few hundred milliseconds, with more complex queries taking only a few minutes. Test 7: Run all 99 queries, 64 at a time - Concurrency = 64. A common mis-perception is Hadoop and Hive are slow, but with the introduction of Hive LLAP and various. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. These sessions are aimed at people with dementia and their carers. Upon receiving the query results, javascript on client browser will parse the data locally to visualize. How many servers do you have in your Hadoop Cluster? How much Data are you pumping through those servers? What kind of queries are you running? In absolute terms, your answer depends on those questions in complex ways that are unlikely to receive. 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. As long as the queries would have really returned the same plan, this is a big performance winner. 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. Windows Registry Hive Location You can fix slow computer up by deleting the unneeded files because of your hard dr. Hive 3 new features. 0 has a great UX and various extra functionalities to help you make SQL queries run faster. There could be many reasons why Drill is running slow in a specific environment. Running a query similar to the following shows significant performance when a subset of rows match filter select count(c1) from t where k in (1% random k's) Following chart shows query in-memory performance of running the above query with 10M rows on 4 region servers when 1% random keys over the entire range passed in query IN clause. Many Hadoop users get confused when it comes to the selection of these for managing database. Low latency (interactive) queries on historical data: enable faster decisions - E. The latest Tweets from Sarah Collins (@SarahCollinsNB). You can vote up the examples you like. Hive: Hive View allows the user to write & execute SQL queries on the cluster. Tez is also part of the execution engine for Hive LLAP. Applications should not depend on the output format of the EXPLAIN QUERY PLAN command. This advanced Hive Concept and Data File Partitioning Tutorial cover an overview of data file partitioning in hive like Static and Dynamic Partitioning. A few, sometimes just one, of the reducers seem to run for much longer than the others. method identifier in here to see where the slow query is coming from. For simple queries like SELECT * with limit, it is much faster. Hive is a good tool for. For more advanced stats collection need to run analyze table queries. Please also include the best way to contact you about your query, such as your contact phone number or contact address. So, directly writing the INSERT OVERWRITE query results to S3 is an optimization that Qubole Hive offers you. If our data is not inside MySQL you can’t use “sql” to query it. Hive was developed by the folks at Facebook in 2008, as a means of providing an easy-to-use, SQL-like. LLAP enables application development and IT infrastructure to run queries that return real-time. After a few queries in Hive, I started to find Hadoop slow compared to my expectation (and slow compared to Netezza). (10 replies) Cluster Information: Total of 5 Nodes in the cluster - with CDH42 installed by RPM and impala beta. Forecast Cloudy – Why Is My Azure Table Storage Query So Slow Again? Perhaps this post shouldn’t exist as I already profiled basics of Azure Table Storage in my previous post. This means your pc will run so slow it are hard to obtain anything over. Step 4: Start MySQL because Hive needs it to connect to the metastore and because Spark SQL will also need it when it connects to Hive. Very often users need to filter the data on specific column values. 0 Goal: This article introduces the new feature -- Hive transaction based on the behavior of Hive 1. The script you provided does show an improvement in IO and CPU time, but you are comparing apples and oranges here. 4rc1 and I'm seeing some pretty slow queries using. For basic stats collection turn on the config hive. 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. The query you posted is the exact exception I stated earlier. How many concurrent queries can it support? Certainly not 100K concurrent clients He is using a wrong metric to make a conclusion Hive/Hadoop is very very slow Hive/Hadoop needs to be fixed to reduce query latency But an existing DBMS cannot replace Hive/Hadoop. It can also be due to bad table defination. When someone has dementia, music is one of the most powerful and effective ways to stimulate communication and interaction, to prompt memory and brighten mood. 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. If you have trouble connecting to Hive from clients using JDBC or ODBC drivers, check for errors in the hive-server2 logs:. (internal) Minimal increase rate in the number of partitions between attempts when executing take operator on a structured query. Note: When using Native Query mode, the driver executes the HiveQL query to retrieve the result set metadata for SQLPrepare. Starting with Hive 1. 0, EXPLAIN EXTENDED output for queries can be logged at the INFO level by setting the hive. log and mysql-slow. Need some configuration to install. You can use the Hive Query executor with any event-generating stage where the logic suits your needs. Running a query similar to the following shows significant performance when a subset of rows match filter select count(c1) from t where k in (1% random k's) Following chart shows query in-memory performance of running the above query with 10M rows on 4 region servers when 1% random keys over the entire range passed in query IN clause. 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. Both have the same condition. Test 7: Run all 99 queries, 64 at a time - Concurrency = 64. It's interactive, fun, and you can do it with your friends. With the recent release of Cloudera 6. 24 responses on " Impala Performance Update: Now Reaching DBMS-Class Speed " Steve Smith January 13, 2014 at 10:56 am. 94, hadoop 1. Each Hive query is translated to at least one. Learn 5 ways to make your Apache Hive queries run faster on your Hadoop cluster. This happens because the namenode needs to load all the records from the metastore into memory. Hive Query Running Slow. Hive is bad at ad-hoc query, unless you really, really need Hive’s scale and low license cost.