Azure PowerShell support for managing HDInsight resources using Azure Service Manager is deprecated, and was removed on January 1, 2017. These tables can be queried using pycopg2 library. 0 has a great UX and various extra functionalities to help you make SQL queries run faster. Partitioning Tables Hive partitioning is an effective method to improve the query performance on larger tables. 94, hadoop 1. 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. But you can also run Hive queries using Spark SQL. In this tutorial, we will be executing two queries on this dataset. Use custom SQL to connect to a specific query rather than the entire data source. 0, the HBase Hive integration only supported querying primitive data types in columns. Still if you need quick result, you have to login to impala-shell instead of Hive and run your query. Windows Registry Hive Location You can fix slow computer up by deleting the unneeded files because of your hard dr. Hive has some fancy ways to do do sampling of data, but it doesn't work on external tables. We will see below on how we can configure Hive Connector properties of both Generated SQL and User-defined SQL. Second, column-oriented storage options can be quite helpful. See the Tableau Knowledge Base for detailed instructions on connecting Hive data to Tableau. To connect to a data source, see Import data from external data sources. 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. The Hive Query Language is a subset of SQL-92. A data scientist’s perspective. 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 second query will be able to read directly from the persisted data instead of having to read in the entire dataset again. 14 minute read. 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. Slow window function query with big table I'm doing some performance testing on a new DB design on PostgreSQL 9. For Hive I'm using the ORCFile format with Cost Based Optimisation turned on and the execution engine is TEZ, so performance is good. Improving or tuning hive… August 13, 2015 By Mohammad Farooq 1. I am new to Hadoop Hive and I am developing a reporting solution. Hadoop Tutorials: Ingesting XML in Hive using XPath Author Intel Business Published on August 15, 2013 In the first of my series of Hadoop tutorials, I wanted to share an interesting case that arose when I was experiencing poor performance trying to do queries and computations on a set of XML Data. Lower values might lead to longer execution times as more jobs will be run. See Description of HIVE-9481 for examples. All the columns have the string, character varying data-type for Hive, Impala, Spark and Drill. But like you said such queries are slow. Without partitioning Hive reads all the data in the directory and applies the query filters on it. But you can also run Hive queries using Spark SQL. Hive translate your query into temporary Map/Reduce job and that job executed on behalf of your hive query. The SQL AND condition and OR condition can be combined to test for multiple conditions in a SELECT, INSERT, UPDATE, or DELETE statement. You should use wmf database (instead of the wmf_raw database) if you can, or your queries will be slow. 1, queries executed against table 'default. select count(*) from foo limit 1 uses mapreduce and takes over a minute. PostgreSQL database. 0 onward supports storing and querying Avro objects in HBase columns by making them visible as structs to Hive. Two python scripts, HQL_SELECT. 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. 208e and SparkSQL 2. stop the Spark ThriftServer from the Ambari console. If you have access to a server with SQL*Plus, you can run the query there in the background. Troubleshoot Apache Hive by using Azure HDInsight. But like you said such queries are slow. Data serving layer. Still if you need quick result, you have to login to impala-shell instead of Hive and run your query. create table foo as select * from bar limit 1 uses mapreduce and takes forever. Resolution Steps Option 1. To apply the partitioning in hive, users need to understand the domain of the data on which they are doing analysis. 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. There could be many reasons why Drill is running slow in a specific environment. For all databases that I know, reading that volume is as close as 1 minute. 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. QuerySurge Database Backup Procedures QuerySurge is backed by a MySQL database. Once the data is loaded into the table, you will be able to run HiveQL statements to query this data. Hive is extensible with UDFs. Hive or Pig? People often ask why do Pig and Hive exist when they seem to do much of the same thing. New features and changes are introduced for IBM InfoSphere Information Server, Version 11. 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. No, you could not use Hive to join table sitting in MySQL/Oracle with table in HDFS. This means that when running an incorrect query (with incorrect or non-existing field names) the Hive tables will be populated with NULL instead of throwing an exception. The SQL UNION Operator. log mysql-slow. Get details of the new additions in Ambari's Hive View. py and SQL_SELECT. 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. This means your pc will run so slow it are hard to obtain anything over. 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. We hope this blog helped you in running Hive queries through Java programs. We ask for a question, then wait for an answer. To apply the partitioning in hive, users need to understand the domain of the data on which they are doing analysis. Query using dplyr syntax. stagingdir is set to "/tmp/hive", Hive will simply do a RENAME operation which will be instant. commit phase has been running for almost 16 hours and has not finished yet. Impala State Store - The state store coordinates information about all instances of impalad running in your environment. destURL WHERE V. Get details of the new additions in Ambari's Hive View. stop the Spark ThriftServer from the Ambari console. Please also include the best way to contact you about your query, such as your contact phone number or contact address. See the Tableau Knowledge Base for detailed instructions on connecting Hive data to Tableau. Other query systems within Facebook, such as Hive  and Peregrine , query data that is written to HDFS with a long (typ-ically one day) latency before data is made available to queries and queries themselves take. All the above functions are present in Apache Hive 0. A slow running Hive query is usually a sign of sub-optimal configuration. Menu Compressing Text Tables In Hive 01 June 2011 on hadoop, hive, ruby At Forward we have been using Hive for a while and started out with the default table type (uncompressed text) and wanted to see if we could save some space and not lose too much performance. Spark (and Hadoop/Hive as well) uses “schema on read” – it can apply a table structure on top of a compressed text file, for example, (or any other supported input format) and see it as a table; then we can use SQL to query this “table. Windows Registry Hive Location You can fix slow computer up by deleting the unneeded files because of your hard dr. A simple solution I came up with involves simply piping your Hive query to the command line. > > -Jake >. One thing that Intelligence was able to discover however, was the frequency of which the controllers of this hive mind exerted their influence with. To avoid this latency, Impala avoids Map Reduce and access the data directly using specialized distributed query engine similar to RDBMS. 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. Monitor your rigs from a single dashboard. A data scientist’s perspective. Multi Table Inserts minimize the number of data scans required. Yet many queries run on Hive have filtering where clauses limiting the data to be retrieved and processed, e. log You would notice in the logs that hive brings up a spark client to run the queries. Use the comparison to determine whether metadata caching will be useful. " This don't seem to be the case on my machine. You can vote up the examples you like. I have one base query named Sales that is a Fact table from SQL. Analysis 3. Then you will get the main reason. Yet, you are waiting which very slow computer start off up or run multiple programs. You can use Sqoop to import data from a relational database management system (RDBMS) such as MySQL or Oracle or a mainframe into the Hadoop Distributed File System (HDFS), transform the data in Hadoop MapReduce, and then export the data back into an RDBMS. 94, hadoop 1. LLAP is optimized for queries that involve joins and aggregates. Microsoft Access / VBA Forums on Bytes. 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. QTEZ-330: Parallel Hive queries on Hive 2. 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. It maybe due to priority and you run during peak time. After a few queries in Hive, I started to find Hadoop slow compared to my expectation (and slow compared to Netezza). txt Run queries in Cron. After query compilation, HiveServer2 generates a Tez graph that is submitted to YARN. make table and even select and it is till 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. Aside from queries, Hive can be a good choice if you’d like to write feature-rich, fault-tolerant, batch (i. Each column in the batch is represented as a vector of a primitive data type. This can be rather exasperating for the fact that the reason for purchasing a computer is to hasten it. This method allows you to set an identifier on an operation. So, I guess it. With Presto, the social media giant gave itself a way to query its 300-petabyte data warehouse spread across a massive distributed. Improving or tuning hive query performance is a huge area. Evaluation. The query has been running for several hours and is still not finished. Apache Hive has been an important part of that promise. Second, column-oriented storage options can be quite helpful. 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. There's no way Tableau can influence the data source in question (Hadoop or other) to be faster. A Hive join query takes an inordinately long time, and the console output shows "Reduce=99%" for much of the total execution time. Impala is developed and shipped by Cloudera. Hiveql Group By - Learning Hive Tutorial in simple and easy steps starting from introduction, Installation, Data Types, Create Database, Drop Database, Create Table, Alter Table, Drop Table, Partitioning, Built-in Operators, Hiveql select. This overcomes many of the limitations of the built-in DynamoDB query functionality and makes it significantly more useful for storing raw analytical data. 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. Configure Hive Connector properties for Generated SQL. Spark SQL supports queries that are written using HiveQL, a SQL-like language that produces queries that are converted to Spark jobs. 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. This blog explains how to load the registry hive file NTUSER. Get details of the new additions in Ambari's Hive View. Solution: Aggregate a series of LIKE clauses into one regexp_like expression. , detect & block worms in real-time (a worm may infect 1mil hosts in 1. HIVE :-The Apache Hive ™ data warehouse software facilitates querying and managing large datasets residing in distributed storage. Already 6000+ students are trained in ORIENIT under Mr. > > Hive queries run in many minutes. The strings 'fast' and 'slow' can be supplied to indicate durations of 200 and 600 milliseconds, respectively. Queries, including joins, are. You can vote up the examples you like. stagingdir is set to ". SparkSQL is the slowest on all the three clusters. Make sure to validate your data and keep a close eye on your schema since updates will otherwise go. autogather to true. Hive translate your query into temporary Map/Reduce job and that job executed on behalf of your hive query. You can write your code in dplyr syntax, and dplyr will translate your code into SQL. For comparative study, we benchmarked our findings for Hive queries that analyze an S3 directory with 24000 partitions, each having 1 file. You can use subqueries anywhere that an expression can be used. registerTempTable("comments"), so we can run SQL queries off of it. Owen O'Malley gave a talk at Hadoop Summit EU 2013 about optimizing Hive queries. One thing that Intelligence was able to discover however, was the frequency of which the controllers of this hive mind exerted their influence with. 1, queries executed against table 'default. I'm using Amazon EMR to run Apache Hive queries against an Amazon DynamoDB table. Each Hive query is translated to at least one. hive/kv'; # start a hadoop cluster with 10 nodes and setup ssh proxy to it linux> launch-hadoop-cluster hive-cluster 10 linux> ssh -D xxxx ec2-mumbo-jumbo. Using traditional approach, it make expensive to process large set of data. A data scientist's perspective. A simple solution I came up with involves simply piping your Hive query to the command line. but are very slow. Already 6000+ students are trained in ORIENIT under Mr. Even after running it for hours. 0 each INSERT INTO T can take a column list like INSERT INTO T (z, x, c1). 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. Solution: Aggregate a series of LIKE clauses into one regexp_like expression. The problem is that the query performance is really slow (hive 0. SELECT * WHERE state=’CA’. You can vote up the examples you like. With a fetch task, Hive directly goes to the file and gives the result, rather than start a MapReduce job for the incoming query. registerTempTable("comments"), so we can run SQL queries off of it. This happens because the namenode needs to load all the records from the metastore into memory. As a consequence, the query execution can be slower than expected. I am new to Hadoop Hive and I am developing a reporting solution. 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 see which version of MySQL is installed, run: mysql -V. Programs contains 45000 rows for a total of about 2. 94, hadoop 1. As a data scientist working with Hadoop, I often use Apache Hive to explore data, make ad-hoc queries or build data pipelines. Slow performance can be outside your table too, like stats of dictionary table not timely collected. 7 (latest) One node is namenode and another 4 node is datanode and TT Running on Redhat Linux version 8 HP blades with 48GB memory on each blade. Apache Hive Table Design Best Practices Table design play very important roles in Hive query performance. Here's what I'd suggest - * Check your input split size and adjust the # of mappers for better parallelism. Here is an example of a Hortonworks Hadoop Hive data source using Tableau Desktop on a Windows. Impala State Store - The state store coordinates information about all instances of impalad running in your environment. I have my exercise program ready and I am currently doing it 5 days per week (hour session each time). Cloudera provides the world’s fastest, easiest, and most secure Hadoop platform. nosamplelist if Hive is running in test mode, don't sample. Very often users need to filter the data on specific column values. And start the custom spark-thrift server as below. samplefreq 32 if Hive is running in test mode and table is not bucketed, sampling frequency hive. For basic stats collection turn on the config hive. Without map join, my query run time is 38 seconds. LLAP is optimized for queries that involve joins and aggregates. Hive 3 new features. Improving or tuning hive… August 13, 2015 By Mohammad Farooq 1. Hive Performance Tuning: Below are the list of practices that we can follow to optimize Hive Queries. 0 recommended. just use impala. 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 alternative to the above problem would be to distribute our database load on multiple hosts as the load increases. At the same time, it scales to thousands of nodes and multi hour queries using the Spark engine, which provides full mid-query fault tolerance. In this demo, we will answer the most frequently asked question raised by business analysts--why is my query running slow?. Sqoop is a tool designed to transfer data between Hadoop and relational databases or mainframes. Indexes are made on top of tables so that they speed up queries. 1b rows) which, even in the MapReduce paradigm, took forever since the computational complexity of Cartesian product is usually O(n^2). 1 JDBC JARs are running on HDP 2. output property to true. The strings 'fast' and 'slow' can be supplied to indicate durations of 200 and 600 milliseconds, respectively. Below is some multiple choice Questions corresponding to them are the choice of answers. Cloudera Impala Diagram The Impala solution is composed of the following components : 1. So I have started this year with a goal of running a 10k in mind. This book contains Apache Hive Technical interview questions that you can expect in a Technical interview. 2) to read data from hive tables. Need some expertise here. After a few queries in Hive, I started to find Hadoop slow compared to my expectation (and slow compared to Netezza). This causes the query to be as slow as the time taken by the largest parition’s reducer. Similarly Hive on Tez in HDP 3. The help desk or database team usually hears that described as the application is slow or the database is slow. For simple queries like SELECT * with limit, it is much faster. prefix test_ if Hive is running in test mode, prefixes the output table by this string hive. Suppose the following table as the input. 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. How To Fix A Slow Computer Once you've determined that you carry rid yourself of all unnecessary files, go online. To do so, you should: 1. 13 from which Qubole’s distribution of Hive is derived (we also support Hive 1. Hive JDBC Query too slow: too many fetches after query execution: Kettle Xform I have setup a kettle tranform with only one step "Table Input" that fires a query on a Hive table. 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. Hadoop queries in Pig or Hive can be too slow for real-time data analysis. SQL Server internally tries to automatically turn simple non-parameterized user queries into parameterized queries to take advantage of this performance gain. It could not keep up with the growing data ingestion and query rates. Get details of the new additions in Ambari's Hive View. So far we have seen running Spark SQL queries on RDDs. 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. > > Hive queries run in many minutes. Warning: The data returned by the EXPLAIN QUERY PLAN command is intended for interactive debugging only. Data types. Note that the Spark SQL CLI cannot talk to the Thrift JDBC server. By enabling compression at various phases (i. 1 leading to very slow execution of queries. 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. 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. Learn 5 ways to make your Apache Hive queries run faster on your Hadoop cluster. Make sure to validate your data and keep a close eye on your schema since updates will otherwise go. So iI thought I should share the params that improved the performance of the query. Need some configuration to install. Queries in Hive LLAP are executing slower than expected. With a fetch task, Hive directly goes to the file and gives the result, rather than start a MapReduce job for the incoming query. Spark: Spark 1. Impala is developed and shipped by Cloudera. For INSERT OVERWRITE queries, Qubole Hive allows you to directly write the results to S3. The problem is that the query performance is really slow (hive 0. This post describes the problem of small ORC and Parquet files in HDFS and how it affects Big SQL read performance. Can you please let me know how i can optimize my query and reduce the run time. If all queries select values of type NUMBER, then the returned values have datatype NUMBER. In general, if queries issued against Impala fail, you can try running these same queries against Hive. HiveServer2 allows clients such as Beeline or SQL Workbench/J to run queries against Hive. The SQL UNION Operator. Resolution Steps Option 1. This video shows how to run live analytics using Tableau against Apache Hive LLAP on AWS. 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. Then you will get the main reason. 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. Hive Query Optimization params Date: September 27, 2014 Author: Ankit Bhatnagar 0 Comments Recently I was working a Hive Query and it is seeming running very slow. Our Example. For more information about how to use supported masking functions to mask data stored in Hadoop, see Mask Data Stored in Hadoop. 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. When you have a large data source connected to Tableau through ODBC, you might experience slow performance, particularly while running a large query or creating or refreshing an extract. A few, sometimes just one, of the reducers seem to run for much longer than the others. After the data is loaded, the query select * from should return data. So iI thought I should share the params that improved the performance of the query. Peter McBrien Imperial College London, UK Abstract This article presents benchmarking results1 of two benchmarking sets (run on small clusters of 6 and 9 nodes) applied to Hive and Pig running on Hadoop 0. From Hive to Impala. Click the Save button near the top of the Ambari window. Output to a file beeline -f my_query. 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. Subqueries can be correlated or uncorrelated. To apply the partitioning in hive , users need to understand the domain of the data on which they are doing analysis. 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. If the data is bucketted in hive, you may use hive. 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. Note: When using Native Query mode, the driver executes the HiveQL query to retrieve the result set metadata for SQLPrepare. Hive Vs Impala: 1. Big data face-off: Spark vs. For more information, see HiveServer2 Overview on the Apache Hive website. 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. py, are create to accept SELECT statements from the request. A slow running Hive query is usually a sign of sub-optimal configuration. It allows the DAG to be altered based on the statistics collected. We experiment with the SQL queries, then parameterize them and insert them into a workflow in order to run them together in parallel. Keep track of hashrates, online statuses, GPU errors, team activity, pool configurations, power consumption, remote access from anywhere across the globe, troubleshoot and reboot GPUs remotely or perform bulk updates across your entire farm. Second, column-oriented storage options can be quite helpful. Impala, an ultra-speedy query engine from Cloudera, supercharges Hadoop by avoiding the typical Map-Reduce overhead and parallelizing queries so that they can run on multiple nodes. A Hive join query takes an inordinately long time, and the console output shows "Reduce=99%" for much of the total execution time. log You would notice in the logs that hive brings up a spark client to run the queries. 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. With Presto, the social media giant gave itself a way to query its 300-petabyte data warehouse spread across a massive distributed. 3 Benefits of Apache Hive View 2. Elastic Map Reduce allows you to conveniently run SQL-like queries against DynamoDB using Hive. bucketmapjoin or hive. Please also include the best way to contact you about your query, such as your contact phone number or contact address. ” Computer Running Slow Fix - Clean My PC Serial Registry Optimizer Free 7. Sometimes Amazon Redshift takes hours together to just drop or truncate the tables even if table has very limited rows. Troubleshoot: Open beeline and verify the value of set hive. It also supports a wide range of data formats such. Test 7: Run all 99 queries, 64 at a time - Concurrency = 64. But if you ALTER your hive. Doesn’t putting an extra layer between my application and HBase just slow things down? Actually, no. August 9, 2016. 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. Hive uses Hadoop's Distributed Cache to distribute the added files to all the machines in the cluster at query execution time. The datastage job includes a Hive Connector stage that specifies details about accessing Hive and a sequential file stage where data extracted to. 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. When you use relational database for massive volumes of data , the system starts getting slow in terms of response time. The author of the query does not need to worry about the underlying implementation - Hive handles this automatically. The alternative to the above problem would be to distribute our database load on multiple hosts as the load increases. Very often users need to filter the data on specific column values. Best Practices. Here is an example of a Hortonworks Hadoop Hive data source using Tableau Desktop on a Windows. Deploy the required JAR files and register provided Hive UDFs on the system where Hive is already present. Higher values lead to more partitions read. 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. 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 is how we can run Hive queries through Java programs using hive-jdbc conenctor. One of the biggest challenges Hive users face is the slow response time experienced by end users who are running ad hoc queries. Read Hive Queries - Group By Query & Order By Query. Cloudera provides the world's fastest, easiest, and most secure Hadoop platform. You should use wmf database (instead of the wmf_raw database) if you can, or your queries will be slow. Apache Parquet is a. Slow changing dimensions. Configure Hive Connector properties for Generated SQL. One of the queries is: select a. SELECT * WHERE state=’CA’. Let's write Hive query in a file 'defaultSearchReport. 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 provides a database query interface to Apache Hadoop. The above query groups and orders the query by start_terminal. "Slow" against Hive is pretty much expected - if the data source is slow, Tableau will be slow. 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. 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. The table can then be. 12 supported syntax for 7/10 queries, running between 91. DAT is located in a user's profile and contains all user's registry settings (HKEY_CURRENT_USER). This is Postgres. 3 Benefits of Apache Hive View 2.