Hi, We have HDInsight with interactive query running on our environment. 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. The result of the query is the same as running the one that uses a subquery, and, in particular, the number of MapReduce jobs that Hive creates is the same for both: two in each case, one for each GROUP BY. Suppose the following table as the input. Our Hive extension each_top_k helps running Top-k processing efficiently. In particular, if the table has never been compacted, it will report 0 records, which results in sub-optimal query plans. This reference guide is marked up using AsciiDoc from which the finished guide is generated as part of the 'site' build target. 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 first query is using 11. The above query groups and orders the query by start_terminal. Join Ben Sullins for an in-depth discussion in this video, Why use Hive, part of Analyzing Big Data with Hive. Now I've started mapping again and it seems to take a hell of a lot longer to complete tasks and stuff, like when I rename something it can take up to like 20 seconds just to do that. That means from time to time, we decide that a page isn’t a great reference anymore, and the work to update it is beyond what we can do quickly. A hierarchical query is a type of SQL query that handles hierarchical model data. Hive on Tez is still Hive - it may be faster, but it's still quite slow relative to Impala and Presto. Hive is much simpler and more straight forward and its integration with Slack is preferred. So, I look at Hive as a really accessible way. Upon receiving the query results, javascript on client browser will parse the data locally to visualize. Optimize joins. Impala State Store - The state store coordinates information about all instances of impalad running in your environment. Spark SQL reuses the Hive frontend and MetaStore. The Hive-based pipeline was composed of three logical stages where each stage corresponded to hundreds of smaller Hive jobs sharded by entity_id, since running large Hive jobs for each stage was less reliable and limited by the maximum number of tasks per job. This means backing up the QuerySurge database data directory on a regularly. Use the query profile output available through the PROFILE command in impala-shell or the web UI to verify query execution and timing after running the query. 94, hadoop 1. 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. Due to the overhead of this process, Hive is slow. start/stop/configure/check status of hive, various scripts • conf – Hive environment, metastore, security, and log configuration files • doc – Hive documentation and Hive examples • lib – server’s JAR files • man – man page information • scripts – scripts for upgrading derby and MySQL metastores from one version of Hive to. When using the JDBC jars for Hive 0. So I was able to get Hadoop 2. @Support@ATTWaters. 12 supported syntax for 7/10 queries, running between 91. 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. g "select session_id from app_sessions_prod where 1=1 and session_id = '8043472_2015-05-07 06:55:24' limit 5;" then it is running very slow. First things first: If you have a huge dataset and can tolerate some. The time required to load the data into Hive was less than 1 minute. Data serving layer. Note that 3 of the 7 queries supported with Hive did not complete due to resource issues. When you query large-scale EDW data sets, you have to meet service-level agreement (SLA) benchmarks or other performance expectations. Re-sults measure the runtime (seconds) on a 100-node cluster. It's very important that you know how to improve the performance of query when you are. Note: all. Top 3 Performance Killers For Linked Server Queries […] Updating Statistics in SQL Server: Maintenance Questions & Answers - by Kendra Little - […] Great reason to upgrade! Read more about this issue in Thomas LaRock's article on linked server performance. Apache Hive is an SQL-like software used with Hadoop to give users the capability of performing SQL-like queries on it’s own language, HiveQL, quickly and efficiently. So, there are several Hive optimization techniques to improve its performance which we can implement when we run our hive queries. For Hive managed tables, the data paths come from the warehouse directory. 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. Reason [XXXXX] The query sent to Salesforce. See Replacing the Implementation of Hive CLI Using Beeline and Beeline - New Command Line Shell in the HiveServer2 documentation. The stats for the Aggregate in Fragment 1 that the “slow” query is getting lots of hash collisions compared to the fast query:. You can still edit, merge or append queries as normal while load is disabled. See the Tableau Knowledge Base for detailed instructions on connecting Hive data to Tableau. but are very slow. 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. BlastP simply compares a protein query to a protein database. Parameter use, especially in more complex scenarios, can also cause performance issues. Change query timeout for a pre. Click the Save button near the top of the Ambari window. Conclusion. For example, writing query in hive like this: SELECT COUNT(DISTINCT id) It will always result in using only one reducer that slow. This can happen due to a variety of reasons. 3 Benefits of Apache Hive View 2. I only insert rows to local which got updated since last time I run the insert process (by looking at the unix tiestamp column). Hive on Spark uses yarn-cluster mode, and thus Spark driver is executed in AM. As long as the queries would have really returned the same plan, this is a big performance winner. Q 19 - The difference between the MAP and STRUCT data type in Hive is. PHI-BLAST performs the search but limits alignments to those that match a pattern in the query. In general, if queries issued against Impala fail, you can try running these same queries against Hive. 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. The Hive query execution engine converted this query into MapReduce jobs. 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 data is defined in the Hive Metastore and accessible in the Hadoop cluster, Big SQL can get to it. Filters and query-based input controls that rely on Hadoop-Hive data sources will be slow to populate the list of choices. Each level is effective for a specific scope of queries. Hive only supports structured data. As you can see, there is some boilerplate required by DBI: every connection you create using dbConnect() must at some point be destroyed using dbDisconnect() (or you’ll get a leaked connection, which will slow everything down unnecessarily). But at the scale at which you'd use Hive, you would probably want to move your processing to EC2/EMR for data locality. The wmf database includes filtered and preprocessed data. If the query is complex and long-running you can monitor it's state in the Drill web UI. Request Time. This can happen due to a variety of reasons. I've also been looking at jstack and not sure why it's so slow. a PowerPivot workbook can perform this calculation in <1s), it is still much faster than running this on Pig / Hadoop. Due to new development being focused on HiveServer2, Hive CLI will soon be deprecated in favor of Beeline. A hive query can be a Map Reduce job. Since these can be a chain of events and even timed, the shortcut should allow you to do pretty much whatever you like through Siri, widgets or the shortcuts app. If your schema contains Hive tables, Drill needs to query the Hive metastore. These tables can be queried using pycopg2 library. "Slow" against Hive is pretty much expected - if the data source is slow, Tableau will be slow. 30638 HIVE-64481d5d-eb4f-4a13. For DSS connections which require credentials (most SQL connections, MongoDB, FTP, …), the administrator can configure the connection so that instead of having a global service credential, each user can enter his personal credentials. A - MAP is Key-value pair but STRUCT is series of values. Hive is for analysts with strong SQL skills providing an SQL-like interface and a relational data model. Now I've started mapping again and it seems to take a hell of a lot longer to complete tasks and stuff, like when I rename something it can take up to like 20 seconds just to do that. Hive or Pig? People often ask why do Pig and Hive exist when they seem to do much of the same thing. 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. So, I look at Hive as a really accessible way. The GROUP BY clause is used to group all the records in a result set using a particular collection column. In nearly all parts, we have coded MapReduce jobs to solve specific types of queries (filtering, aggregation, sorting, joining, etc…). 3 for Relational Databases: Reference, Second Edition Tell usHow satisfied are you with SAS documentation?. py, are create to accept SELECT statements from the request. The hive query which is used by my batch is taking too much time to run. So I was able to get Hadoop 2. select /*+ FULL(emp) PARALLEL(emp, 35) */ Here is an example of a "bad" parallel hint because the parallel hint is with an index hint. Suppose the following table as the input. Hive provides a database query interface to Apache Hadoop. It provides an SQL (Structured Query Language) - like language called Hive Query Language (HiveQL). Microsoft Access Outer Join Query: Finding All Records in One Table but Not Another and Creating "Not In" Queries by Molly Pell, Quality Assurance Specialist When querying data from multiple tables in Microsoft Access or SQL Server, we usually use Inner Joins to link records with values that exist in both tables. Need some configuration to install. 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. 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. The query has been running for several hours and is still not finished. Queries in Hive LLAP are executing slower than expected. ,Compute Speed - Hive will be my last option to query vs. In this case, Hive will return the results by performing an HDFS operation (hadoop fs -get equivalent). You find the same issue with top 10 queries so decide to run the individual shard queries run in parallel. old shell> mysqladmin flush-logs. Second, from the result panel, click “export recordset to an external file”. py and SQL_SELECT. Best Practices When Using Athena with AWS Glue. Presto allows querying data where it lives, including Hive, Cassandra, relational databases or even proprietary data stores. Hive is for analysts with strong SQL skills providing an SQL-like interface and a relational data model. Identify access patterns based on Fast reads but slow writes. Here is the list of queries which we use reqularly and how these sql queries can be optimized for better performance. Hive is much simpler and more straight forward and its integration with Slack is preferred. , the CEO, does not report to anyone in the company, therefore, the reportTo column contains the NULL value. The QuerySurge database persists all your QuerySurge data, including QueryPairs, Suites, Scenarios and Results data. Apache Hive is an open source Hadoop application for data warehousing, analysis and querying of large data systems. For now, let's open our saved query SavedQuery1. Oct, 2013, Tartu Running Hive •We will look at it more closely in the practice • Hive query language. When a user selects from a Hive view, the view is expanded (converted into a query), and the underlying tables referenced in the query are validated for permissions. This is slow and expensive since all data has to be read. So far we have seen running Spark SQL queries on RDDs. How to stop Tez jobs. 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. how the data is distributed across the spus, etc There are different ways to check how your netezza box is performing. We currently hold about one and a half petabytes of data as Hive managed tables in HDFS, and the relatively small data size of our important “core_data” tables allows us to use Presto as the default query engine for analysis. Even after running it for hours. Owen O'Malley gave a talk at Hadoop Summit EU 2013 about optimizing Hive queries. We try to keep our site as accurate and up-to-date as possible so that if you’re reading advice here, you can rely on it. Unified Data Access − Load and query data from a variety of sources. HIVE over Spark: SQL-like interface (supports Hive 0. Converting to Columnar Formats (using Hive for conversion) Query tuning. I had one small query as my system is a gravity fed hot water and I called tech support. Hive provides a "schema on read" table query which allows data to be imported quicker than traditional "schema before write" databases. Recently, such SQL-like query languages and their translators [25, 32, 11] have. There are several projects trying to reduces this problem like TEZ from the stinger. Welcome to the Hive Community, where you will find the answers to any questions about Hive smart heating, lighting, camera products and more. In general, if queries issued against Impala fail, you can try running these same queries against Hive. It was designed by Facebook people. the first thing to do is to see how you can tune your queries. 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. Once these operations reach the Hive Driver, Hive tracks their progress through another set of phases: submission, compilation, and execution. Our Hive extension each_top_k helps running Top-k processing efficiently. " This don't seem to be the case on my machine. They are special cases of more general recursive fixpoint queries, which compute transitive closures. Mitigation: Upgrade to a release where this is fixed. 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. While Apache Hive writes data to a temporary location and move them to S3. *Note: In this tutorial, we have configured the Hive Metastore as MySQL. This chapter explains the details of GROUP BY clause in a SELECT statement. Earlier when i fire the same query it took around 5 minutes and now it is taking around 22 minutes. Hive ORC transactional tables that have not been compacted will report an incorrect row count. The editor is used a lot for querying Hive and Impala. 1, queries executed against table 'default. Review a third table called recommendation. This test was across 200GB data (uncompressed) spread across 84 hard drives and 72 hyper threading cores reading it, so I knew it was too slow. Hive relies on MapReduce to execute queries which makes it relatively slow compared to querying engines like Cloudera Impala, Spark or Presto. Hive turns the queries into map-reduce jobs and runs them on hadoop. Queries in Hive LLAP are executing slower than expected. Such queries would need to join the User and Order tables with the Product table. Hiya! This article will explain OUTER and CROSS APPLY and show you how to use them by means of sample code. 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. It was designed by Facebook people. 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. The source for this guide can be found in the _src/main/asciidoc directory of the HBase source. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. 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. The following diagrams depict a simple. You may notice that for queries with only a connection (not loaded locally), is that there is no Table Tools contextual ribbon tabs available for the Query - but these features can all be accessed by right-clicking on the Query in the Workbook Queries pane. Hive Performance - 10 Best Practices for Apache Hive June 26, 2014 by Nate Philip Updated July 13th, 2018 Apache Hive is an SQL-like software used with Hadoop to give users the capability of performing SQL-like queries on it's own language, HiveQL, quickly and efficiently. I'm not sure what the problem is, but seems to be a Hive performance issue when it comes to "highly partitioned" tables. Our Hive extension each_top_k helps running Top-k processing efficiently. An important aspect of unification that our users have consistently requested is the ability to more easily import data stored in external sources, such as Apache Hive. In this case, Hive will return the results by performing an HDFS operation (hadoop fs –get equivalent). If you set the slider to True, this property switches from Hive user to end user only when you run Hive in batch-processing mode. If you have questions about the system, ask on the Spark mailing lists. The above query groups and orders the query by start_terminal. Earlier when i fire the same query it took around 5 minutes and now it is taking around 22 minutes. 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. LLAP is optimized for queries that involve joins and aggregates. Your query ranks 10 million rows. Alternatively, we can migrate the data to Parquet format. The time required to load the data into Hive was less than 1 minute. I cant change the query. Then you can use readLines() and separate out fields based on the '\t' delimiter, creating a data. The users reported that their queries running slow. Apache Tez is a fast data processing engine that can be used as an alternative to slow and old MapReduce. Installing and Configuring the Hive ODBC Driver. 2, so this is 5x more CPU. • A mapping routes incoming queries to pools based on specified factors, such as user name, group, or application. 94h * 50% = ~6h vs 1. The screenshots in the article are a bit out of date, but the procedure is essentially the same when using the driver from SSIS. But you can also run Hive queries using Spark SQL. Hive: Finding Common Wikipedia Words. As you can see, there is some boilerplate required by DBI: every connection you create using dbConnect() must at some point be destroyed using dbDisconnect() (or you’ll get a leaked connection, which will slow everything down unnecessarily). Also the plugin is getting better all the time and should optimize the query better in the future. Then you will get the main reason. Cloudera Impala Diagram The Impala solution is composed of the following components : 1. If hive is used as the interface for accessing TB and PB scale data, it is quite important to optimize the queries to get them to run faster. In this tutorial, you learn how to create a dataframe from a csv file, and how to run interactive Spark SQL queries against an Apache Spark cluster in Azure HDInsight. 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?. Sometimes, however, you won’t want all of the slices to be refreshed - especially if some data is slow moving, or run heavy queries. Queries in Hive LLAP are executing slower than expected. With that being said this practice often results in a table with a lot of partitions, which makes querying a full table or a large part of it a very slow operation. As a consequence, the query execution can be slower than expected. Hive was developed by the folks at Facebook in 2008, as a means of providing an easy-to-use, SQL-like. Such queries would need to join the User and Order tables with the Product table. Assume employee table as. 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. Hadoop Basics VIII: Running SQL Queries with Hive In this part, we will use Hive to execute all the queries that we have been processing since the beginning of this series of tutorials. As the query is running against Hive, here is not the best place to ask. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. Side note: If you bulk-insert rows first and add index and PK constraint (and FK constraint) after, that's going to be much faster, plus you get perfect indexes without bloat without running REINDEX or VACUUM FULL. Query processing speed in Hive is slow but Impala is 6-69 times faster than Hive. Udemy is an online learning and teaching marketplace with over 100,000 courses and 24 million students. Mitigation: Upgrade to a release where this is fixed. With the ease of syntax, constructing the query was simple. Note that 3 of the 7 queries supported with Hive did not complete due to resource issues. But use of the best query is important when performance is considered. To apply the partitioning in hive , users need to understand the domain of the data on which they are doing analysis. Resolution Steps Option 1. And they will query Hive (via pyhs2) and Postgres (via pycopg2) respectively, and return the result in JSON format. hive -e "query goes here" hive -f "path to file" Both of above options can be executed from shellscript. Hive provides data summarization and facilitates query, and analysis of large distributed datasets. Hive Command Line Options. Improving or tuning hive query performance is a huge area. I am not convinced this works One of the main hindrance/reluctance of Hive deployment has been its perceived slow −Query Hive on. As a data scientist working with Hadoop, I often use Apache Hive to explore data, make ad-hoc queries or build data pipelines. When you create your HDInsight cluster, choose the appropriate cluster type to help optimize performance for your workload needs. output property to true. It also makes the Hive client executing the query "memory hungry". 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. So, I look at Hive as a really accessible way. An important aspect of unification that our users have consistently requested is the ability to more easily import data stored in external sources, such as Apache Hive. One of the biggest challenges Hive users face is the slow response time experienced by end users who are running ad hoc queries. Hive is much simpler and more straight forward and its integration with Slack is preferred. To do this, I have created two simple tables in my small cluster called "test" and "test_partitioned". 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. Then you will get the main reason. This video shows how to run live analytics using Tableau against Apache Hive LLAP on AWS. SQL operations in Hive go through a series of states before they return results to the user, such as INITIALIZED, PENDING, and RUNNING. This time the data source connection tested successfully. Introduction to Apache Hive Pelle Jakovits 1. If you have trouble connecting to Hive from clients using JDBC or ODBC drivers, check for errors in the hive-server2 logs:. Queries like the following don't perform well in an Interactive Hive cluster:. Apache Tez is a fast data processing engine that can be used as an alternative to slow and old MapReduce. When using the JDBC jars for Hive 0. It is easy to use and most SQL programmers can instant write some queries. Alternatively, we can migrate the data to Parquet format. So far we have seen running Spark SQL queries on RDDs. You can use the Hive Query executor with any event-generating stage where the logic suits your needs. xml with the hive. As the query is running against Hive, here is not the best place to ask. I am not convinced this works One of the main hindrance/reluctance of Hive deployment has been its perceived slow −Query Hive on. 96 Figure 1: Performance of Shark vs. You may have experienced Microsoft Access query timeout. Running the Spark SQL CLI. Hadoop-Hive data sources are not suitable for creating reports interactively in the Ad Hoc Editor. [KYLIN-614] - find hive dependency shell fine is unable to set the hive dependency correctly [KYLIN-615] - Unable add measures in Kylin web UI [KYLIN-619] - Cube build fails with hive+tez [KYLIN-620] - Wrong duration number [KYLIN-621] - SecurityException when running MR job [KYLIN-627] - Hive tables’ partition column was not sync into Kylin. Hive supports file format of Optimized row columnar (ORC) format with Zlib compression but Impala supports the Parquet format with snappy compression. However the Hive query compiler is still a work in progress, and many optimizations are not completely implemented or completely understood in terms of their interact. The screenshots in the article are a bit out of date, but the procedure is essentially the same when using the driver from SSIS. Close the Hive Shell: You are done with the Hive Shell for now, so close it by entering 'quit;' in the Hive Shell. See the Tableau Knowledge Base for detailed instructions on connecting Hive data to Tableau. Indicates an imminently hazardous situation which, if not avoided, will result in death or serious injury. In the example above, we use DBI to query the database and retrieve the first 5 rows from the City table. 1, queries executed against table 'default. Once these operations reach the Hive Driver, Hive tracks their progress through another set of phases: submission, compilation, and execution. log, you can use a series of commands like this: shell> cd mysql-data-directory shell> mv mysql. Until recently, optimizing Hive queries focused mostly on data layout techniques such as partitioning and bucketing or using custom file. Hive 2 is much faster, but still not as fast as relational databases. A query can complete in seconds, where it might take an hour in hive. Until recently, optimizing Hive queries focused mostly on data layout techniques such as partitioning and bucketing or using custom file. 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. MySQL Limit Query - How to Limit Query Results. Each Hive query is translated to at least one. Installing and Configuring the Hive ODBC Driver. What a query like this does require is running many queries, one for each minute. Self-service exploratory analytics is one of the most common use cases we see by our customers running on Cloudera’s Data Warehouse solution. From the first galance of this problem, I can see that you have a lot of "like" operators in your query. With Looker and BigQuery, you can scale query processing power and storage independently and elastically, allowing for fast exploration over massive datasets. 5 Tips for efficient Hive queries with Hive Query Language Hive on Hadoop makes data processing so straightforward and scalable that we can easily forget to optimize our Hive queries. Maybe it also can be one of its cons, because all of those features can increase bugs and load times. The maximum size of the result set from a join query is the product of the number of rows in all the joined tables. Trying to test the ODBC connection or query it in Crystal Reports. Queries involving join operations often require more tuning than queries that refer to only one table. @Support@ATTWaters. Hive, like Pig, is an abstraction on top of MapReduce and when run, Hive translates queries into a series of MapReduce jobs. Hive also stores query logs on a per Hive session basis in /tmp//, but can be configured in hive-site. 30638 HIVE-64481d5d-eb4f-4a13. QuerySurge Database Backup Procedures QuerySurge is backed by a MySQL database. The required information is retrieved by manual parsing methods instead of a query language. We currently hold about one and a half petabytes of data as Hive managed tables in HDFS, and the relatively small data size of our important “core_data” tables allows us to use Presto as the default query engine for analysis. 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. 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. 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?. Schema-RDDs provide a single interface for efficiently working with structured data, including Apache Hive tables, parquet files and JSON files. 10, hbase 0. Sometimes it is useful to limit the number of rows that are returned from an SQL query. However, the original Hive 1 server has high latency with access times on the order of 30 seconds and up to 2 minutes. the query plan starts a bunch of reducers but the data from the each partition goes to a single reducer. For DSS connections which require credentials (most SQL connections, MongoDB, FTP, …), the administrator can configure the connection so that instead of having a global service credential, each user can enter his personal credentials. Speed up your Hive queries. Use the query profile output available through the PROFILE command in impala-shell or the web UI to verify query execution and timing after running the query. Indicates an imminently hazardous situation which, if not avoided, will result in death or serious injury. Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. You may have experienced Microsoft Access query timeout. Troubleshoot Apache Hive by using Azure HDInsight. In the example above, we use DBI to query the database and retrieve the first 5 rows from the City table. Until recently, optimizing Hive queries focused mostly on data layout techniques such as partitioning and bucketing or using custom file. code STAD to analyze the transaction time. In S3, moving data is expensive (involves copy and delete operations). A data scientist's perspective. Instead, the Spark application would be kept running and used by subsequent queries submitted in the same Hive session, until the session is closed. Spark, Hive, Impala and Presto are SQL based engines. Close the Hive Shell: You are done with the Hive Shell for now, so close it by entering 'quit;' in the Hive Shell. Now I've started mapping again and it seems to take a hell of a lot longer to complete tasks and stuff, like when I rename something it can take up to like 20 seconds just to do that. 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. As long as the queries would have really returned the same plan, this is a big performance winner. It sits on top of only the Hadoop Distributed File System. Speed up your Hive queries. Optimizing the queries is directly related to infrastructure, size of data, organization of data, storage formats and the data readers/ processors. Also, we can control number of map/reducer for better performance by rewriting query. See Replacing the Implementation of Hive CLI Using Beeline and Beeline - New Command Line Shell in the HiveServer2 documentation. Converting to Columnar Formats (using Hive for conversion) Query tuning. Hive Performance - 10 Best Practices for Apache Hive June 26, 2014 by Nate Philip Updated July 13th, 2018 Apache Hive is an SQL-like software used with Hadoop to give users the capability of performing SQL-like queries on it's own language, HiveQL, quickly and efficiently. The samples included here use a clean installation of the Hortonworks Sandbox and query some of the sample tables included out of the box. Then you will get the main reason. A query will be translated into multiple MapReduce jobs, and submitted to Hadoop for execution. The download page for the Hive ODBC driver provides a link to an article on how to configure the Hive ODBC driver to connect to HDInsight using Excel. Note that 3 of the 7 queries supported with Hive did not complete due to resource issues. If Hive impersonation is disabled and / or Apache Sentry is used, a malicious user might use any of the Hive xpath UDFs to expose the contents of a file on the node that is running HiveServer2 which is owned by the HiveServer2 user (usually hive). Fixed by pushing down the limit clause to the Oracle source. Apache Parquet is a. This is going to take some time, even when read from the index directly and no sort step. By specifying the time range to query, you avoid reading unnecessary data and can thus speed up your query significantly. I have one base query named Sales that is a Fact table from SQL. the first thing to do is to see how you can tune your queries. start/stop/configure/check status of hive, various scripts • conf – Hive environment, metastore, security, and log configuration files • doc – Hive documentation and Hive examples • lib – server’s JAR files • man – man page information • scripts – scripts for upgrading derby and MySQL metastores from one version of Hive to. A declarative query language would greatly simplify the query interface, reduce the programming effort and boost developer productivity. old shell> mysqladmin flush-logs. Hive turns the queries into map-reduce jobs and runs them on hadoop. You can use these function within query you have requirement to calculate cumulative SUM or AVG. Building a unified platform for big data analytics has long been the vision of Apache Spark, allowing a single program to perform ETL, MapReduce, and complex analytics.