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Hive Query Running Slow
"Slow" against Hive is pretty much expected - if the data source is slow, Tableau will be slow. 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. 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. Yet many queries run on Hive have filtering where clauses limiting the data to be retrieved and processed, e. 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. Efficient Top-k Query Processing using each_top_k. 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?. what you be obliged to do first though is always to remove any programs and files you also do not need. Big-Bench hive does not work on the plain CSV files, but instead transforms the files into the ORC file format, more efficient and native to hive. Use the Hive Query executor in an event stream. “Extremely easy to use, no problems in downloading and running the program. A few, sometimes just one, of the reducers seem to run for much longer than the others. HiveQL • Hive query language provides the basic SQL like operations. Instead, the Spark application would be kept running and used by subsequent queries submitted in the same Hive session, until the session is closed. My issue is that returning the data to Power BI is extremely slow. 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. log, you can use a series of commands like this: shell> cd mysql-data-directory shell> mv mysql. Hive is bad at ad-hoc query, unless you really, really need Hive’s scale and low license cost. The file NTUSER. Comparison of Hive's query optimisation techniques. Here’s what I’d suggest - * Check your input split size and adjust the # of mappers for better parallelism. 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. This allows Hive to perform ad hoc analysis of HBase data which can be deeply structured. In this case, Hive will return the results by performing an HDFS operation (hadoop fs -get equivalent). For example, suppose that your data is located at the following S3 paths:. 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. Read Hive Queries - Group By Query & Order By Query. xml file mentioned in the first step. Within each value of start_terminal, it is ordered by start_time, and the running total sums across the current row and all previous rows of duration_seconds. Here are some tips on how to manage their resource. I'm running into. 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 !. Owen O'Malley gave a talk at Hadoop Summit EU 2013 about optimizing Hive queries. 0 is the slowest on both clusters not because some queries fail with a timeout, but because almost all queries just run slow. Durations are given in milliseconds; higher values indicate slower animations, not faster ones. I am new to Hadoop Hive and I am developing a reporting solution. Slow window function query with big table I'm doing some performance testing on a new DB design on PostgreSQL 9. Schema-RDDs provide a single interface for efficiently working with structured data, including Apache Hive tables, parquet files and JSON files. Home Big Data Hive query failed with error: Killing the Job. This overcomes many of the limitations of the built-in DynamoDB query functionality and makes it significantly more useful for storing raw analytical data. Your only protection against data loss is a regular backup schedule. 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. Hive Query's are running slow hours! for a single wave of all 30 queries). 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. Please suggest the correct way to investigate this issue or kindly suggest any resolution. If a query fails against both Impala and Hive, it is likely that there is a problem with your query or other elements of your environment: Review the Language Reference to ensure your query is valid. When running SQL from within another programming language the results will be returned as a Dataset/DataFrame. 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. Hive Query’s are running slow hours! for a single wave of all 30 queries). Without map join, my query run time is 38 seconds. If using default Spark in IOP. As a result, SQLPrepare might be slow. If a user poses a query that cannot be answered by the local database alone, ANGIE calls the appropriate. Reports based on Hadoop-Hive are not suitable for dashboards. This is because we use the DATEDIFF function on the column appointment_date. Thanks to the inimitable pgAdminIII for the Explain graphics. 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. This allows Hive to perform ad hoc analysis of HBase data which can be deeply structured. My issue is that returning the data to Power BI is extremely slow. Hive Query's are running slow hours! for a single wave of all 30 queries). Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. This protects you from SQL injection attacks, and as an added benefit, the database can often optimise the query so it runs faster. Also will build up your confidence in Hive. SQL Server internally tries to automatically turn simple non-parameterized user queries into parameterized queries to take advantage of this performance gain. Hive allows only appends, not inserts, into tables, so the INSERT keyword simply instructs Hive to append the data to the table. No, you could not use Hive to join table sitting in MySQL/Oracle with table in HDFS. …It just gives you a more robust. Big Data maybe different, like Aster,Hive, Pig etc. Impala is a n Existing query engine like Apache Hive has run high run time overhead, latency low throughput. As of Hive 1. In my previous blog post, I wrote about using Apache Spark with MySQL for data analysis and showed how to transform and analyze a large volume of data (text files) with Apache Spark. 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. If you have access to a server with SQL*Plus, you can run the query there in the background. 3 Benefits of Apache Hive View 2. But they still want to complete the query first even if it's slightly slow because they're not motivated enough to learn how to tweak/optimize queries before getting results. 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. Big data face-off: Spark vs. The hive loading stage is not only "moving" file in hdfs from the data/ dir into the hive/warehouse. SELECT * FROM precipitation_data; Indexing. Without map join, my query run time is 38 seconds. In this case, we’re comparing each date to any date less than or equal to it in order to calculate the running total. The script you provided does show an improvement in IO and CPU time, but you are comparing apples and oranges here. I've been monitoring jmap, and don't believe it's a memory or gc issue. The cost-based optimizer (CBO) tries to generate the most efficient join order. – Tom Harrison Jr Apr 17 '16 at 19:43 |. 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. > > Hive queries run in many minutes. Presto is an open-source distributed SQL query engine that is designed to run SQL queries even of petabytes size. mapResourceReqt: 1638 maxContainerCapability:1200″ How to determine the cause of a simple COUNT(*) query to run slow Unable to import Oracle table with CLOB column into HDFS using Sqoop. For more information about how to use supported masking functions to mask data stored in Hadoop, see Mask Data Stored in Hadoop. Once the data is loaded into the table, you will be able to run HiveQL statements to query this data. See Query that produces a huge result is slow topic later in this article. Best Practices When Using Athena with AWS Glue. Multi Table Inserts minimize the number of data scans required. Very often users need to filter the data on specific column values. Multi Table Inserts minimize the number of data scans required. (10 replies) Cluster Information: Total of 5 Nodes in the cluster - with CDH42 installed by RPM and impala beta. Slow window function query with big table I'm doing some performance testing on a new DB design on PostgreSQL 9. 0 is the slowest on both clusters not because some queries fail with a timeout, but because almost all queries just run slow. 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. Note: This database type only support equal (=) join operations. 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. Reverse engineering from Hive database processing is slow due to the absence of system tables. Schema-RDDs provide a single interface for efficiently working with structured data, including Apache Hive tables, parquet files and JSON files. The hive query which is used by my batch is taking too much time to run. HiveQL • Hive query language provides the basic SQL like operations. Hive is written in Java but Impala is written in C++. We tried to query segment geo spatial data from hive directly for real time update but found it very slow. DAT is located in a user's profile and contains all user's registry settings (HKEY_CURRENT_USER). It also provides graphical view of the query execution plan. Hive can insert data into multiple tables by scanning the input data just once (and applying different query operators) to the input data. 1, queries executed against table 'default. txt Run queries in Cron. Some background information: I'm working with Dataiku DSS, HDFS, and partitioned datasets. It maybe due to priority and you run during peak time. Using Hive for Analytical Queries Hi, and welcome to this course on Writing Complex Analytical Queries with Hive. Hive Query’s are running slow hours! for a single wave of all 30 queries). 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. A slow running Hive query is usually a sign of sub-optimal configuration. Resting is a buff status effect which restores player health and prevents the depletion of player hunger. But at the scale at which you'd use Hive, you would probably want to move your processing to EC2/EMR for data locality. Here are some tips on how to manage their resource. The problem is that the query performance is really slow (hive 0. Run SQL directly on Hadoop Single Commercial DB cluster limited to ~32 nodes SQL on Hadoop scales to thousands of machines Data in Commercial DB <<< Data in HDFS HDFS holds all the data Hive on the PB scale Hadoop Warehouse is **SLOW** Try Presto. The maximum size of the result set from a join query is the product of the number of rows in all the joined tables. Until recently, optimizing Hive queries focused mostly on data layout techniques such as partitioning and bucketing or using custom file. 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. In this case, we're comparing each date to any date less than or equal to it in order to calculate the running total. ALTER TABLE ADD PARTITION. Scroll down until the start_terminal value changes and you will notice that running_total starts over. See Description of HIVE-9481 for examples. The second query will be able to read directly from the persisted data instead of having to read in the entire dataset again. QTEZ-330: Parallel Hive queries on Hive 2. The required information is retrieved by manual parsing methods instead of a query language. The example data set to demonstrate Hive query language optimization 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. Spark SQL can also be used to read data from an existing Hive installation. So let's! Today I'll go and analyse the data contained in multiple CSV files. Without map join, my query run time is 38 seconds. Execute the appropriate Hive UDFs using the Hive query language. You can use the Hive Query executor with any event-generating stage where the logic suits your needs. (Cloudera mentioned. Here is the performance enhancement piece. These tables can be queried using pycopg2 library. For #1: Send the query to your DBA and have them run an EXPLAIN to understand why the query might be taking a long time to run. It explores possible solutions using existing tools to compact small files in larger ones with the goal of improving read performance. To avoid this latency, Impala avoids Map Reduce and access the data directly using specialized distributed query engine similar to RDBMS. In this case, Hive will return the results by performing an HDFS operation (hadoop fs -get equivalent). Windows Registry Hive Location You can fix slow computer up by deleting the unneeded files because of your hard dr. Here's what I'd suggest - * Check your input split size and adjust the # of mappers for better parallelism. …And again remember Presto can work with Hive…in fact it kind of is built in…and so it works really well. It has a neat SQL Engine, and is very versatile. To do this, please run below commands before the query:. This means your pc will run so slow it are hard to obtain anything over. The query you posted is the exact exception I stated earlier. 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. For instance, it takes up to an hour to return a "table" of about 151M records in Power BI. In this case, the results of the Hive query might not reflect changes made to the data while the query was running. The queries then get translated into MapReduce for execution on a Hadoop cluster, but that process is inherently slower than running a SQL query directly against a relational database, according to Gualtieri. Hive of course is a good choice for queries that lend themselves to being expressed in SQL, particularly long-running queries where fault tolerance is desirable. slow to query• Often best to denormalize during load – Write once, read many. 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. Such queries would need to join the User and Order tables with the Product table. 203e and Spark 2. 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. See Query that produces a huge result is slow topic later in this article. SQL Server internally tries to automatically turn simple non-parameterized user queries into parameterized queries to take advantage of this performance gain. Need some configuration to install. Following query can be used to retrieve data from precipitation_data. This is slow and expensive since all data has to be read. what you be obliged to do first though is always to remove any programs and files you also do not need. 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. Article The queries were run on an in-h ouse Hadoop cluster that. 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. As a result, SQLPrepare might be slow. The Netezza JDBC driver may detect a batch insert, and under the covers convert this to an external table load. I've also been looking at jstack and not sure why it's so slow. Query using dplyr syntax. 203e and Spark 2. " Presto and Impala did a little better than the other engines in terms of concurrency, or how many SQL queries can it run simultaneously. The required information is retrieved by manual parsing methods instead of a query language. If you continue browsing the site, you agree to the use of cookies on this website. Whether it is for OS time, Network time , Buffer time or other. 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. Hive Query Running Slow. The Hortonworks Hive ODBC Driver with SQL Connector interrogates Hive to obtain schema information to present to a SQL-based application. 70+ channels, more of your favorite shows, & unlimited DVR storage space all in one great price. 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. Hive: Cloudera Cluster: Apache Hive 1. This is not because some queries fail with a timeout, but because almost all queries just run slow. But at the scale at which you'd use Hive, you would probably want to move your processing to EC2/EMR for data locality. 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. 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. Apache Hive is a popular tool in Data warehouse solutions based on Hadoop. mapResourceReqt: 1638 maxContainerCapability:1200″ How to determine the cause of a simple COUNT(*) query to run slow Unable to import Oracle table with CLOB column into HDFS using Sqoop. Send log file with remote_syslog2. 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. : Select, update and insert operation. 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. don't use an obviously slow data format for Hive. This is a big deal for big data, because with Impala, querying. The problem is that the query performance is really slow (hive 0. 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. This causes the query to be as slow as the time taken by the largest parition’s reducer. Please suggest the correct way to investigate this issue or kindly suggest any resolution. If all queries select values of type NUMBER, then the returned values have datatype NUMBER. It provides a simple SQL-like language called Hive Query Language (HQL) for querying and analyzing the data stored in Hadoop clusters. One way to export SQL Server data to CSV is by using the SQL Server Import and Export Wizard. create table foo as select * from bar limit 1 uses mapreduce and takes forever. Starting with Hive 1. py and SQL_SELECT. Hive : Hive is one of the component of Hadoop built on top of Hadoop Distributed File System and is a data ware house kind of system in Hadoop. They have ten limbs in total, the front two are clawed, talon-like graspers, the second pair of limbs are semi-prehensile wings, like that of a bat, the third and center pair of arms are legs, with geko-like feet capable of sticking to and climbing most surfaces with ease, the next pair is another set of leathery wings, and the last pair of limbs are two more legs with geko-like feet. This blog explains how to load the registry hive file NTUSER. The Spark SQL CLI is a convenient tool to run the Hive metastore service in local mode and execute queries input from the command line. Kalyan, Cloudera CCA175 Certified Consultant, Apache Contributor, 12+ years of IT exp, IIT Kharagpur, Gold Medalist. 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. You have to play disk defragmenter to fix this circumstance. Test 6: Run all 99 queries, 32 at a time - Concurrency = 32. Including Hive queries in an Oozie workflow is a pretty common use case with recurrent pitfalls as seen on the user group. The view is getting all the records from the Hive table without any WHERE clause. The Netezza JDBC driver may detect a batch insert, and under the covers convert this to an external table load. Join – A Hive query may try to scan the. Click Service Actions > Restart All. By enabling compression at various phases (i. So, they ask us how to improve the queries but it's hard work for us. So I was able to get Hadoop 2. The help desk or database team usually hears that described as the application is slow or the database is slow. In nearly all parts, we have coded MapReduce jobs to solve specific types of queries (filtering, aggregation, sorting, joining, etc…). In this article, I have put together the steps I usually follow when Amazon Redshift Drop and Truncate Table Running Slow. Our Hive extension each_top_k helps running Top-k processing efficiently. As a data scientist working with Hadoop, I often use Apache Hive to explore data, make ad-hoc queries or build data pipelines. I'm using Amazon EMR to run Apache Hive queries against an Amazon DynamoDB table. In general, if queries issued against Impala fail, you can try running these same queries against Hive. Apache Hive Table Design Best Practices Table design play very important roles in Hive query performance. 0 has a great UX and various extra functionalities to help you make SQL queries run faster. Such queries would need to join the User and Order tables with the Product table. Big-Bench models a set of long running analytic queries. 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. Hive; HIVE-18009; Multiple lateral view query is slow on hive on spark. 94, hadoop 1. Performance tuning in amazon redshift - Simple tricks The performance tuning of a query in amazon redshift just like any database depends on how much the query is optimised, the design of the table, distribution key and sort key, the type of cluster (number of nodes, disk space,etc) which is basically the support hardware of redshift, concurrent queries, number of users, etc. One use of Spark SQL is to execute SQL queries. 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. This is slow and expensive since all data has to be read. 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. To connect to a data source, see Import data from external data sources. We will see below on how we can configure Hive Connector properties of both Generated SQL and User-defined SQL. Indexes are made on top of tables so that they speed up queries. bucketmapjoin. 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. Many Hadoop users get confused when it comes to the selection of these for managing database. Execute the appropriate Hive UDFs using the Hive query language. And they will query Hive (via pyhs2) and Postgres (via pycopg2) respectively, and return the result in JSON format. CBO does not support all operators, such as "sort by," scripts, and table functions. You can use the Hive Query executor with any event-generating stage where the logic suits your needs. While the query is running, in another terminal you can follow the hive cli log by using tail -f /tmp/hive/hive. Since MapR snapshots are guaranteed to be consistent, your read queries on a snapshot will see a completely static view of your Hive tables. So we built JSON file for each segment from geo­map visualization. An alternative to running ‘show tables’ or ‘show. By enabling compression at various phases (i. At the same time this language also allows traditional map/reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL. 3 Benefits of Apache Hive View 2. Parameterized queries. 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. enable is enabled. py and SQL_SELECT. create table foo as select * from bar limit 1 uses mapreduce and takes forever. This overcomes many of the limitations of the built-in DynamoDB query functionality and makes it significantly more useful for storing raw analytical data. 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. I cant change the query. 0 onward supports storing and querying Avro objects in HBase columns by making them visible as structs to Hive. Hive Query Running Slow. So, I guess it. Hive "loading"-stage is slow. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. So, there are several Hive optimization techniques to improve its performance which we can implement when we run our hive queries. 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. This video shows how to run live analytics using Tableau against Apache Hive LLAP on AWS. 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. Instead, the Spark application would be kept running and used by subsequent queries submitted in the same Hive session, until the session is closed. The longest time to finish the workload. Simply install it alongside Hive. MicroStrategy Simba Hive Driver couldn't be loaded on RHEL 72. (10 replies) Cluster Information: Total of 5 Nodes in the cluster - with CDH42 installed by RPM and impala beta. Avoid Exceeding Throughput. 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 video shows how to run live analytics using Tableau against Apache Hive LLAP on AWS. A suitable Spark version may not be included (yet) in your chosen hadoop. Ok, on a past blog we've been setuping Azure HDInsight for some Hive fun. The help desk or database team usually hears that described as the application is slow or the database is slow. 0 has a great UX and various extra functionalities to help you make SQL queries run faster. Reverse engineering from Hive database processing is slow due to the absence of system tables. 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. Usage odbcGetInfo(channel) Arguments channel connection handle as returned by odbcConnect of class "RODBC". Indexes are made on top of tables so that they speed up queries. I'm also guessing that the SPDE engine for HDFS will be using MapReduce rather than Tez? But I'm unsure how to confirm this when running a query via SAS. Apache Hive performance monitoring from DRIVEN monitors your HQL queries across all your Hadoop clusters for better big data management. What is Hive? Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. Some background information: I'm working with Dataiku DSS, HDFS, and partitioned datasets. This information is used to find data so the distributed resources can be used to respond to queries. Big-Bench hive does not work on the plain CSV files, but instead transforms the files into the ORC file format, more efficient and native to hive. 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. In this article, I have put together the steps I usually follow when Amazon Redshift Drop and Truncate Table Running Slow. 12 supported syntax for 7/10 queries, running between 91. Tools like Impala and Hawq provide interfaces that enable end users to write queries in the SQL programming language. Without partitioning Hive reads all the data in the directory and applies the query filters on it. log You would notice in the logs that hive brings up a spark client to run the queries. This causes the query to be as slow as the time taken by the largest parition's reducer. 0 on Tez is fast enough to outperform Presto 0. For all databases that I know, reading that volume is as close as 1 minute. (10 replies) Cluster Information: Total of 5 Nodes in the cluster - with CDH42 installed by RPM and impala beta. Hadoop MapReduce in Python vs. > > Hive queries run in many minutes. Evaluation. Skip to main content. "dynamic" columns in Hive larry ogrodnek - 24 Feb 2011 One of the presentations at the HBase meetup the other night was on building a query language on top of HBase. First things first: If you have a huge dataset and can tolerate some. Reports based on Hadoop-Hive are not suitable for dashboards. My issue is that returning the data to Power BI is extremely slow. And start the custom spark-thrift server as below. Let's write Hive query in a file 'defaultSearchReport. log You would notice in the logs that hive brings up a spark client to run the queries. txt Run queries in Cron. Windows Registry Hive File - Fix Slow PC Performance In Windows 7. The Hive query execution engine converted this query into MapReduce jobs. It could not keep up with the growing data ingestion and query rates. 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. Hive: Hive View allows the user to write & execute SQL queries on the cluster. 10, hbase 0. The script you provided does show an improvement in IO and CPU time, but you are comparing apples and oranges here. Note that 3 of the 7 queries supported with Hive did not complete due to resource issues. EXISTS and NOT EXISTS are used with a subquery in WHERE clause to examine if the result the subquery returns is TRUE or FALSE. The strings 'fast' and 'slow' can be supplied to indicate durations of 200 and 600 milliseconds, respectively. To do so, you should: 1. Hive allows only appends, not inserts, into tables, so the INSERT keyword simply instructs Hive to append the data to the table. How To Fix A Slow Computer Once you've determined that you carry rid yourself of all unnecessary files, go online. To connect to a data source, see Import data from external data sources. But you can also run Hive queries using Spark SQL. This part of the series will show you how to use a loop to execute a query multiple times, using a different value in the WHERE clause of the query each time. > Hive should only be used in cases where you can't fetch data from the CF > directly, using say CQL. When the query finishes, Hive doesn't terminate this spark application. Troubleshoot Apache Hive by using Azure HDInsight. Results: As outlined in the above results, Interactive Query is a super optimized engine for running concurrent queries. See Description of HIVE-9481 for examples. why? i shouldn't have to analyze simple queries like this to find workarounds that make them reasonably performant. Spark, Hive, Impala and Presto are SQL based engines. If no query selects values of type BINARY_DOUBLE but any query selects values of type BINARY_FLOAT, then the returned values have datatype BINARY_FLOAT. 7 or greater on your computer. Get details of the new additions in Ambari's Hive View. However, in last few months I heard quite a bit of complaints from customers attempting to query Azure tables of performance. 4 installed on both machines, got hdfs, yarn, hive etc running successfully. The problem is that the query performance is really slow (hive 0. enable is enabled. Capability to run big queries We limit queries by their runtime and the data they process on Presto. To demonstrate this technique we're going to list the number of films which have won different numbers of Oscars.