Showing posts with label hive. Show all posts
Showing posts with label hive. Show all posts

June 07, 2022

Top 20 Amazon EMR Interview Questions and Answers


    Using open source frameworks like as Apache Spark, Apache Hive, and Presto, Amazon EMR is the industry-leading cloud big data platform for data processing, interactive analysis, and machine learning. With EMR, you can perform petabyte-scale analysis for half the price of typical on-premises solutions and over 1.7 times quicker than ordinary Apache Spark.

AWS(Amazon Web Services) Interview Questions and Answers

AWS Cloud Interview Questions and Answers

Ques. 1): What are the benefits of using Amazon EMR?


Amazon EMR frees you up to focus on data transformation and analysis rather than maintaining computing resources or open-source apps, and it saves you money. You may supply as much or as little capacity on Amazon EC2 as you want using EMR, and build up scaling rules to handle changing compute demand. CloudWatch notifications may be set up to notify you of changes in your infrastructure so you can react quickly. You may use EMR to submit your workloads to Amazon EKS clusters if you utilise Kubernetes. Whether you employ EC2 or EKS, EMR's optimised runtimes help you save time and money by speeding up your analysis.

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Ques. 2): How do I troubleshoot a query that keeps failing after each iteration?


You may use the same tools that they use to troubleshoot Hadoop Jobs in the case of a processing failure. The Amazon EMR web portal, for example, may be used to locate and view error logs. Here's where you can learn more about troubleshooting an EMR task.

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Ques. 3): What is the best way to create a data processing application?


In Amazon EMR Studio, you can create, display, and debug data science and data engineering applications written in R, Python, Scala, and PySpark. You may also create a data processing task on your desktop and run it on Amazon EMR using Eclipse, Spyder, PyCharm, or RStudio. When spinning up a new cluster, you may also pick JupyterHub or Zeppelin in the software configuration and build your application on Amazon EMR utilising one or more instances.

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Ques. 4): Is it possible to perform many queries in a single iteration?


Yes, you may specify a previously ran iteration in subsequent processing by specifying the option. The approach ensures that subsequent runs on the same iteration use the exact same input records from the Kinesis stream as earlier runs.

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Ques. 5): In Amazon EMR, how is a computation done?


The Hadoop data processing engine is used by Amazon EMR to perform calculations using the MapReduce programming methodology. The customer uses the map() and reduce() methods to create their algorithm. A customer-specified number of Amazon EC2 instances, consisting of one master and several additional nodes, are started by the service. On these instances, Amazon EMR runs Hadoop software. The master node separates the input data into blocks and distributes the block processing to the subordinate nodes. The map function is then applied to the data that has been assigned to each node, resulting in intermediate data. The intermediate data is then sorted and partitioned before being transmitted to processes on the nodes that perform the reduction function locally.

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Ques. 6): What distinguishes EMR Studio from EMR Notebooks?


There are five major differences:

EMR Studio does not require access to the AWS Management Console. The EMR Studio server is not part of the AWS Management Console. If you don't want data scientists or engineers to have access to the AWS Management Console, this is a good option.

To log in to EMR Studio, you can utilise enterprise credentials from your identity provider using AWS Single Sign-On (SSO).

EMR Studio provides you with your first notebook encounter. Because EMR Studio kernels and applications operate on EMR clusters, you receive the benefit of distributed data processing with the Amazon EMR runtime for Apache Spark, which is designed for performance.

Attaching the laptop to an existing cluster or establishing a new one is all it takes to run code on a cluster.

EMR Studio features a user interface that is simple to use and abstracts hardware specifications. For instance, you can create cluster templates once and then utilise them to create future clusters.

EMR Studio facilitates debugging by allowing you to access native application user interfaces in one location with as few clicks as feasible.

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Ques. 7): What tools are available to me for debugging?


You may use a variety of tools to gather information about your cluster and figure out what went wrong. If you utilise Amazon EMR studio, you can leverage debugging tools like Spark UI and YARN Timeline Service. You can gain off-cluster access to persistent application user interfaces for Apache Spark, Tez UI, and the YARN timeline server through the Amazon EMR Console, as well as multiple on-cluster application user interfaces and a summary view of application history for all YARN apps. You may also use SSH to connect to your Master Node and inspect cluster instances using these web interfaces. See our docs for additional details.

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Ques. 8): What are the advantages of utilising Command Line Tools or APIs rather than the AWS Management Console?


The Command Line Tools or APIs allow you to programmatically launch and monitor the progress of running clusters, as well as build custom functionality for other Amazon EMR customers (such as sequences with multiple processing steps, scheduling, workflow, or monitoring) or build value-added tools or applications. The AWS Management Console, on the other hand, offers a simple graphical interface for starting and monitoring your clusters from a web browser.

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Ques. 9): What distinguishes EMR Studio from SageMaker Studio?


With Amazon EMR, you may utilise both EMR Studio and SageMaker Studio. EMR Studio is an integrated development environment (IDE) for developing, visualising, and debugging data engineering and data science applications in R, Python, Scala, and PySpark. Amazon SageMaker Studio is a web-based visual interface that allows you to complete all machine learning development phases in one place. SageMaker Studio provides you total control, visibility, and access to every step of the model development, training, and deployment process. You can upload data, create new notebooks, train and tune models, travel back and forth between phases to change experiments, compare findings, and push models to production all in one spot, increasing your productivity significantly.

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Ques. 10): Is it possible to establish or open a workspace in EMR Studio without a cluster?


Yes, a workspace may be created or opened without being attached to a cluster. You should only join them to a cluster when you need to execute. EMR Studio kernels and apps run on Amazon EMR clusters, allowing you to take advantage of distributed data processing with the Amazon EMR runtime for Apache Spark.

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Ques. 11): What computational resources can I use in EMR Studio to execute notebooks?


You may execute notebook code on Amazon EMR on Amazon Elastic Compute Cloud (Amazon EC2) or Amazon EMR on Amazon Elastic Kubernetes Service using EMR Studio (Amazon EKS). Notebooks can be added to either existing or new clusters. In EMR Studio, you can construct EMR clusters in two ways: by using an AWS Service Catalog pre-configured cluster template or by defining the cluster name, number of instances, and instance type.

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Ques. 12): What IAM policies are required to utilise EMR Studio?


To interact with other AWS services, each EMR studio requires permissions. Your administrators must build an EMR Studio service role using the specified policies to grant the essential access to your EMR Studios. They must also create a user role for EMR Studio that defines permissions at the Studio level. They may assign a session policy to a user or group when they add users and groups from AWS Single Sign-On (AWS SSO) to EMR Studio to apply fine-grained authorization constraints. Administrators may utilise session policies to fine-tune user rights without having to create several IAM roles. See Policies and Permissions in the AWS Identity and Access Management User Guide for further information on session policies.

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Ques. 13): What may EMR Notebooks be used for?


EMR Notebooks make it simple to create Apache Spark apps and conduct interactive queries on your EMR cluster. Multiple users may build serverless notebooks straight from the interface, attach them to an existing shared EMR cluster, or provision a cluster and begin playing with Spark right away. Notebooks can be detached and reattached to new clusters. Notebooks are automatically saved to S3 buckets, and you may access them from the console to resume working. The libraries contained in the Anaconda repository are preconfigured in EMR Notebooks, allowing you to import and utilise them in your notebooks code to modify data and show results. Furthermore, EMR notebooks feature built-in Spark monitoring capabilities, allowing you to track the status of your Spark operations and debug code directly from the notebook.

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Ques. 14): Is Amazon EMR compatible with Amazon EC2 Spot, Reserved, and On-Demand Instances?


Yes. On-Demand, Spot, and Reserved Instances are all supported by Amazon EMR.

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Ques. 15): What role do Availability Zones play in Amazon EMR?


All nodes for a cluster are launched in the same Amazon EC2 Availability Zone using Amazon EMR. Running a cluster in the same zone enhances work flow performance. By default, Amazon EMR runs your cluster in the Availability Zone with the greatest available resources. You can, however, define a different Availability Zone if necessary. You may also utilise On-Demand Capacity Reservations to optimise your allocation for the lowest-priced on-demand instances, best spot capacity, or lowest-priced on-demand instances.

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Ques. 16): What are node types in a cluster?


There are three sorts of nodes in an Amazon EMR cluster:

master node : A master node supervises the cluster by executing software components that coordinate the distribution of data and tasks among the other nodes for processing. The master node keeps track of task progress and oversees the cluster's health. A master node is present in every cluster, and it is feasible to establish a single-node cluster using only the master node.

core node : A core node is a node that contains software components that conduct jobs and store data in your cluster's Hadoop Distributed File System (HDFS). At least one core node exists in multi-node clusters.

task node: A task node is a node that only performs tasks and does not store data in HDFS. Task nodes are not required.

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Ques. 17): Can Amazon EMR restore a cluster's master node if it goes down?


Yes. You may set up an EMR cluster with three master nodes (version 5.23 or later) to offer high availability for applications like YARN Resource Manager, HDFS Name Node, Spark, Hive, and Ganglia. If the primary master node fails or important processes, such as Resource Manager or Name Node, crash, Amazon EMR immediately switches to a backup master node. You may run your long-lived EMR clusters without interruption since the master node is not a potential single point of failure. When a master node fails, Amazon EMR immediately replaces it with a new master node that has the same configuration and boot-strap activities.

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Ques. 18): What are the steps for configuring Hadoop settings for my cluster?


For most workloads, the EMR default Hadoop setup is sufficient. However, depending on the memory and processing needs of your cluster, changing these values may be necessary. If your cluster activities are memory-intensive, for example, you may want to employ fewer tasks per core and limit the size of your job tracker heap. A pre-defined Bootstrap Action is offered to configure your cluster on starting in this case. For setup information and usage instructions, see the Developer's Guide's Configure Memory Intensive Bootstrap Action. You may also use an extra preset bootstrap action to tailor your cluster parameters to whatever value you like.

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Ques. 19): Is it possible to modify tags directly on Amazon EC2 instances?


Yes, tags may be added or removed directly on Amazon EC2 instances in an Amazon EMR cluster. However, because Amazon EMR's tagging system does not immediately sync changes to a corresponding Amazon EC2 instance, we do not advocate doing so. To guarantee that the cluster and its associated Amazon EC2 instances have the necessary tags, we recommend using the Amazon EMR GUI, CLI, or API to add and delete tags for Amazon EMR clusters.

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Ques. 20): How does Amazon EMR operate with Amazon EKS?


Amazon EMR requires you to register your EKS cluster. Then, using the CLI, SDK, or EMR Studio, send your Spark tasks to EMR. The Kubernetes scheduler on EKS is used by EMR to schedule Pods. EMR on EKS creates a container for each task you perform. The container includes an Amazon Linux 2 base image with security updates, as well as Apache Spark and its dependencies, as well as your application's particular needs. Each Job is contained within a pod. This container is downloaded and executed by the Pod. If the container's image has already been deployed to the node, the download is skipped and a cached image is utilised instead. Log or metric forwarders, for example, can be deployed as sidecar containers to the pod. When the job finishes, the Pod finishes as well. You may continue debug the task using Spark UI after it has finished.

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May 11, 2022

Top 20 Apache Pig Interview Questions and Answers


            Pig is an Apache open-source project that runs on Hadoop and provides a parallel data flow engine. It contains the pig Latin language, which is used to express data flow. It includes actions such as sorting, joining, filtering, and scripting UDF (User Defined Functions) for reading, writing, and processing. Pig stores and processes the entire task using Map Reduce and HDFS.

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Ques. 1): What benefits does Pig have over MapReduce?


The development cycle for MapReduce is extremely long. It takes a long time to write mappers and reducers, compile and package the code, submit tasks, and retrieve the results. Dataset joins are quite complex to perform. Low level and stiff, resulting in a large amount of specialised user code that is difficult to maintain and reuse is difficult.

Pig does not require the compilation or packaging of code. Pig operators will be turned into maps or jobs will be reduced internally. Pig Latin supports all common data-processing procedures, as well as high-level abstraction for processing big data sets.

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Ques. 2): Is Piglatin a Typographically Strong Language? If so, how did you arrive at your conclusion?


In a strongly typed language, the type of all variables must be declared up front. When you explain the schema of the data in Apache Pig, it expects the data to be in the same format.

When the schema is unknown, however, the script will adjust to the actual data types at runtime. PigLatin can thus be described as firmly typed in most circumstances but gently typed in others, i.e. it continues to work with data that does not meet its expectations.

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Ques. 3): What are Pig's disadvantages?


Pig has a number of flaws, including:

Pig isn't the best choice for real-time applications.

When you need to get a single record from a large dataset, Pig isn't very useful.

It works in batches since it uses MapReduce.

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Ques. 4): What is Pig Storage, exactly?


Pig comes with a default load function called Pig Storage. Additionally, we may use pig storage to import data from a file system into the pig.

While loading data into pig storage, we may also provide the data delimiter (how the fields in the record are separated). We can also provide the data's schema as well as the data's type.

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Ques. 5): Explain Grunt in Pig and its characteristics.


The Grunt takes on the role of an Interactive Shell Pig. Grunt's main characteristics are:

To move the cursor to the end of a line, press the ctrl-e key combination.

As a Grunt retains command history, the lines in the history buffer can be recalled using the up and down cursor keys.

Grunt supports the auto-completion method by attempting to finish Pig Latin keywords and functions when the Tab key is hit.

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Ques. 6): What Does Pig Flatten Mean?


When there is data in a tuple or a bag, we may use the Flatten modifier in Pig to remove the level of nesting from that data. Un-nests bags and tuples should be flattened. The Flatten operation for tuples will substitute the fields of a tuple for a tuple, however un-nesting bags is a little more complicated because it necessitates the creation of new tuples.

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Ques. 7): Can you distinguish between logical and physical plans?


Pig goes through a few processes while converting a Pig Latin Script into MapReduce jobs. Pig generates a logical plan after performing basic parsing and semantic testing. Pig's logical plan, which is executed during execution, describes the logical operators. Pig then generates a physical plan. The physical plan specifies the physical operators required to execute the script.

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Ques. 8): In Pig, what does a co-group do?


Co-group unites the data collection by grouping only one of the data sets. It then groups the elements by their common field and provides a set of records with two distinct bags. The records of the first data set with the common data set are in the first bag, and the records of the second data set with the same data set are in the second bag.

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Ques. 9): Explain the bag.


Pig includes several data models, including a bag. The bag is an unorganised collection of tuples with possibly duplicates that is used to store collections while they are being grouped. The size of the bag is equal to the size of the local disc, implying that the bag's size is limited. When the bag is full, Pig will empty it onto the local disc and only maintain a portion of it in memory. It is not necessary for the entire bag to fit into memory. With ", we signify bags.

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Ques. 10): Can you describe the similarities and differences between Pig and Hive?


Both Hive and Pig have similar characteristics.

Both internally transform the commands to MapReduce.

High-level abstractions are provided by both technologies.

Low-latency queries are not supported by either.

OLAP and OLTP are not supported by either.

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Ques. 11): How do Apache Pig and SQL compare?


The use of Apache Pig for ETL, lazy evaluation, storing data at any stage in the pipeline, support for pipeline splits, and explicit specification of execution plans set it apart from SQL. SQL is built around queries that return only one result. SQL doesn't have a built-in mechanism for separating a data processing stream into sub-streams and applying various operators to each one.

User code can be added at any step in the pipeline with Apache Pig, whereas with SQL, data must first be put into the database before the cleaning and transformation process can begin.

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Ques. 12): Can Apache Pig Scripts Join Multiple Fields?


Yes, several fields can be joined in PIG scripts since join procedures take records from one input and combine them with records from another. This is accomplished by specifying the keys for each input and joining the two rows when the keys are equal.

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Ques. 13): What is the difference between the commands store and dumps?


After running the dump command, the data appears on the console, but it is not saved. Whereas the output is executed in a folder and the store is stored in the local file system or HDFS. Most hadoop developers utilised the'store' command to store data in HDFS in a protected environment.

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Ques. 14):  Is 'FUNCTIONAL' a User Defined Function (UDF)?


No, the keyword 'FUNCTIONAL' does not represent a User Defined Function (UDF). Some functions must be overridden while using UDF. You must certainly complete your tasks using only these functions. However, because the keyword 'FUNCTIONAL' is a built-in function (a pre-defined function), it cannot be used as a UDF.


Ques. 15): Which method must be overridden when writing evaluate UDF?


When developing UDF in Pig, we must override the method exec(). While the base class may change, when developing filter UDF, we must extend FilterFunc, and when writing evaluate UDF, we must extend EvalFunc. EvaluFunc is parameterized, and the return type must be specified as well.


Ques. 16): What role does MapReduce play in Pig programming?


Pig is a high-level framework that simplifies the execution of various Hadoop data analysis problems. A Pig Latin programme is similar to a SQL query that is executed using an execution engine. The Pig engine can convert programmes into MapReduce jobs, with MapReduce serving as the execution engine.


Ques. 17): What Debugging Tools Are Available For Apache Pig Scripts?


The essential debugging utilities in Apache Pig are describe and explain.

When trying to troubleshoot or optimise PigLatin scripts, Hadoop developers will find the explain function useful. In the grunt interactive shell, explain can be applied to a specific alias in the script or to the entire script. The explain programme generates multiple text-based graphs that can be printed to a file.

When building Pig scripts, the describe debugging utility is useful since it displays the schema of a relation in the script. Beginners learning Apache Pig can use the describe utility to see how each operator alters data. A pig script can have multiple describes.


Ques. 18): What are the relation operations in Pig? Explain any two with examples.


The relational operations in Pig:

foreach, order by, filters, group, distinct, join, limit.foreach: It takes a set of expressions and applies them to all records in the data pipeline to the next operator.A =LOAD ‘input’ as (emp_name :charrarray, emp_id : long, emp_add : chararray, phone : chararray, preferences : map [] );B = foreach A generate emp_name, emp_id;Filters: It contains a predicate and it allows us to select which records will be retained in our data pipeline.

Syntax: alias = FILTER alias BY expression;

Alias indicates the name of the relation, By indicates required keyword and the expression has Boolean.

Example: M = FILTER N BY F5 == 50;


Ques. 19): What are some Apache Pig use cases that come to mind?


The Apache Pig large data tools are used for iterative processing, raw data exploration, and standard ETL data pipelines. Pig is commonly used by researchers who want to use the data before it is cleansed and placed into the data warehouse because it can operate in situations where the schema is unknown, inconsistent, or incomplete.

It can be used by a website to track the response of users to various sorts of adverts, photos, articles, and so on in order to construct behaviour prediction models.


Ques. 20): In Apache Pig, what is the purpose of illustrating?


Illustrate is used to run Pig scripts on large datasets, which might take a long time. That is why developers run pig scripts on sample data, even though it is probable that the sample data selected will not execute the script correctly. If the script includes a join operator, for example, there must be a small number of records in the sample data with the same key, or the join operation will fail. Developers manage these issues by using the function illustrate, which takes data from the sample and ensures that some records pass through while others are restricted by modifying records in such a way that they follow the condition set whenever it encounters operators like the filter or join, which remove data. Illustrate displays each step's output but does not run MapReduce operations.




April 28, 2022

Top 20 Apache Drill Interview Questions and Answers


        Apache Drill is an open source software framework that enables the interactive study of huge datasets using data demanding distributed applications. Drill is the open source version of Google's Dremel technology, which is provided as a Google Big Query infrastructure service. HBase, MongoDB, MapR-DB, HDFS, MopEDS, AmazonS3, Google cloud storage, Swift, NAS, and local files are among the NoSQL databases and filesystems it supports. Data from various datastores can be combined in a single query. You may combine a user profile collection in MongoDB with a directory of Hadoop event logs, for example.

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Ques. 1): What is Apache Drill, and how does it work?


Apache Drill is an open-source SQL engine with no schema that is used to process massive data sets and semi-structured data created by new age Big data applications. Drill's plug-and-play interface with Hive and Hbase installations is a great feature. Google's Dremel file system inspired the Apache Drill. We may have a faster understanding of data analysis without having to worry about schema construction, loading, or any other type of maintenance that used to be required in the RDBMS system. We can easily examine multi-structured data with Drill.

Apache Drill is a schema-free SQL Query Engine for Hadoop, NoSQL, and Cloud Storage that allows us to explore, visualise, and query various datasets without needing to use ETL or other methods to fix them to a schema.

Apache Drill can also directly analyse multi-structured and nested data in non-relational data stores, without any data restrictions.

The schema-free JSON model is included in Apache Drill, the first distributed SQL query engine and its looks like -

  • Elastic Search
  • MongoDB
  • NoSQL database

The Apache Drill is very useful for those professionals that already working with SQL databases and BI tools like Pentaho, Tableau, and Qlikview.

Also Apache Drill supports to -

  • RESTful,
  • ANSI SQL and
  • JDBC/ODBC drivers

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Ques. 2): Is Drill a Good Replacement for Hive?


Hive is a batch processing framework that is best suited for processes that take a long time to complete. Drill outperforms Hive when it comes to data exploration and business intelligence.

Drill is also not exclusive to Hadoop. It can, for example, query NoSQL databases (such as MongoDB and HBase) and cloud storage (eg, Amazon S3, Google Cloud Storage, Azure Blob Storage, Swift).

Both Instruments Hive and Drill are used to query enormous datasets; Hive is best for batch processing for long-running processes, whereas Drill offers more advancement and a better user experience. Drill's limitation isn't limited to Hadoop; it may also access and process data from other sources.

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Ques. 3): What are the differences between Apache Drill and Druid?


The primary distinction is that Druid pre-aggregates metrics to give low latency queries and minimal storage use.

You can't save information about individual events while using Druid to analyse event data.

Drill is a generic abstraction for a variety of NoSql data stores. Because the values in these data stores are not pre-aggregated and are saved individually, they can be used for purposes other than storing aggregated metrics.

Drill does not provide the low latency queries required to create dynamic reporting dashboards.

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Ques. 4): What does Tajo have in common with Apache Drill?


Tajo resembles Drill in appearance. They do, however, have a lot of differences. Their origins and eventual purposes are the most significant contrasts. Drill is based on Google's Dremel, whereas Tajo is based on the combination of MR and parallel RDBMS. Tajo's goal is a relational and distributed data warehousing system, whereas Drill's goal is a distributed system for interactive analysis of large-scale datasets.

As far as I'm aware, the first Drill contains the following characteristics:

  • Drill is a Google Dremel clone project.
  • Its primary goal is to do aggregate queries using a full table scan.
  • Its main goal is to handle queries quickly.
  • It employs a hierarchical data model.

Tajo, on the other hand, has the following features:

  • Tajo combines the benefits of MapReduce and Parallel databases.
  • It primarily targets complex data warehouse queries and has its own distributed query evaluation approach.
  • Its major goal is scalable processing by exploiting the advantages of MapReduce and Parallel databases.
  • We expect that sophisticated query optimization techniques, intermediate data streaming, and online aggregation will significantly reduce query response time.
  • It utilizes a relational data model. We feel that the relational data model is sufficient for modelling the vast majority of real-world applications.
  • Tajo is expected to be linked with existing BI and OLAP software.

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Ques. 5): What are the benefits of using Apache Drill?


Some of the most compelling reasons to use Apache Drill are listed below.

  • Simply untar the Apache Drill and use it in local mode to get started. It does not necessitate the installation of infrastructure or the design of a schema.
  • Running SQL queries does not necessitate the use of a schema.
  • We can query semi-structured and complex data in real time with Drill.
  • The SQL:2003 syntax standard is supported by Apache Drill.
  • Drill can be readily linked with BI products like QlikView, Tableau, and MicroStrategy to give analytical capabilities.
  • We can use Drill to conduct an interactive query that will access the Hive and HBase tables.
  • Drill supports multiple data stores such as local file systems, distributed file systems, Hadoop HDFS, Amazon S3, Hive tables, HBase tables, and so on.
  • Apache Drill can be easily scalable from a single system up to 1000 nodes.

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Ques. 6): What Are the Great Features of Apache Drill?


The following features are -

  • Schema-free JSON document model similar to MongoDB and Elastic search
  • Code reusability
  • Easy to use and developer friendly
  • High performance Java based API
  • Memory management system
  • Industry-standard API like ANSI SQL, ODBC/JDBC, RESTful APIs
  • How does Drill achieve performance?
  • Distributed query optimization and execution
  • Columnar Execution
  • Optimistic Execution
  • Pipelined Execution
  • Runtime compilation and code generation
  • Vectorization

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Ques. 7): What are some of the things we can do with the Apache Web interface?


The tasks that we can conduct through the Apache Drill Web interface are listed below.

  • The SQL Queries can be conducted from the Query tab.
  • We have the ability to stop and restart running queries.
  • We can view the executed queries by looking at the query profile.
  • In the storage tab, you can view the storage plugins.
  • In the log tab, we can see logs and stats.

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Ques. 8): What is Apache Drill's performance like? Does the number of lines in a query result affect its performance?


We utilise drill for its rest server and connect D3 visualisation for querying IOT data, and the querying command(select and join) suffers from a lot of slowness, however this was fixed when we switched to spark SQL.

Drill is useful in that it can query most data sources, but it may need to be tested before being used in production. (If you want something faster, I believe you can find a better query engine.) But for development and testing, it's been quite useful.

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Ques. 9): What Data Storage Plugins does Apache Drill support?


The following is a list of Data Storage Plugins that Apache Drill supports.

  • File System Data Source Storage Plugin
  • HBase Data Source Storage Plugin
  • Hive Data Source Storage Plugin
  • MongoDB Data Source Storage Plugin
  • RDBMS Data Source Storage Plugin
  • Amazon S3 Data Source Storage Plugin
  • Kafka Data Source Storage Plugin
  • Azure Blob Data Source Storage Plugin
  • HTTP Data Source Storage Plugin
  • Elastic Search Data Source Storage Plugin

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Ques. 10): What's the difference between Apache Solr and Apache Drill, and how do you use them?


The distinction between Apache Solr and Apache Drill is comparable to that between a spoon and a knife. In other words, despite the fact that they deal with comparable issues, they are fundamentally different instruments.

To put it plainly... Apache Solr is a search platform, while Apache Drill is a platform for interactive data analysis (not restricted to just Hadoop). Before performing searches with Solr, you must parse and index the data into the system. For Drill, the data is stored in its raw form (i.e., unprocessed) on a distributed system (e.g., Hadoop), and the Drill application instances (i.e., drillbits) will process it in parallel.

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Ques. 11): What is the recommended performance tuning approach for Apache Drill?


To tune Apache Drill's performance, a user must first understand the data, query plan, and data source. Once these locations have been discovered, the user can utilise the performance tuning technique below to increase the query's performance.

  • Change the query planning options if necessary.
  • Change the broadcast join options as needed.
  • Switch the aggregate between one and two phases.
  • The hash-based memory-constrained operators can be enabled or disabled.
  • We can activate query queuing based on your needs.
  • Take command of the parallelization.
  • Use partitions to organise your data.

Apache Storm Interview Questions and Answers

Ques. 12): What should you do if an Apache Drill query takes a long time to deliver a result?


Check the following points if a query from Apache Drill is taking too long to deliver a result.

  • Check the query's profile to determine if it's moving or not. The query progress is determined by the time of the latest update and change.
  • Streamline the process where Apache Drill is taking too long.
  • Look for partition pruning and projection pushdown operations.


Ques. 13): I'm using Apache Drill with one drillbit to query approximately 20 GB of data, and each query takes several minutes to complete. Is this normal?


The performance of a single bit drill is determined by the Java memory setup and resources available on the computer where your query is being performed. Because the query engine must identify meaningful matches, the where clause requires more work from the query engine, which is why it is slower.

You can also alter JVM parameters in the drill configuration. You can devote more resources to your searches, which should result in speedier results.


Ques. 14): How does Apache Drill compare to Apache Phoenix with Hbase in terms of performance?


Because Drill is a distributed query engine, this is a fascinating question. In contrast, Phoenix implements RDBMS semantics in order to compete with other RDBMS. That isn't to suggest that Drill won't support inserts and other features... But, because they don't do the same thing right now, comparing their performance isn't really apples-to-apples.

Drill can query HBase and even push query parameters down into the database. Additionally, there is presently a branch of Drill that can query data stored in Phoenix.

Drill can simultaneously query numerous data sources. Logically if you choose to use Phoenix, you could use both to satisfy your business needs.


Ques. 15): Is Apache Drill 1.5 ready for usage in production?


Drill is one of the most mature SQL-on-Hadoop solutions in general. As with all of the SQL-on-Hadoop solutions, it may or may not be the best fit for your use case. I mention that solely because I've heard of some extremely far-fetched use cases for Drill that aren't a good fit.

Drill will serve you well in your production environment if you wish to run SQL queries without "requiring" ETL first.

Any tool that supports the ODBC and JDBC connections can easily access it as well.


Ques. 16): Why doesn't Apache Drill get the same amount of attention as other SQL-on-Hadoop tools?


To keep track of SQL on Hadoop tools and to advise enterprise customers on which ones would be ideal for them. A lot of SQL on Hadoop solutions have a large number of users. Presto has been used by a number of major Internet firms (Netflix, AirBnB), as well as a number of large corporations. It is largely sponsored by Facebook and Teradata (my job). The Cloudera distribution makes Impala widely available. Phoenix and Kylin also make a lot of appearances and have a lot of popularity. Until it doesn't function or a flaw is discovered, Spark SQL is the go-to for new projects these days. Hive is the hard to beat incumbent. Adoption is crucial.


Ques. 17): Is it possible to utilise Apache Drill + MongoDB in the same way that RDBMS is used?


To begin, you must comprehend the significance of NoSQL. To be honest, deciding between NoSQL and RDBMS based on a million or ten million users is not a great number.

However, as you stated, the size of your dataset will only grow. You can begin using MongoDB, keeping in mind the scalability element.

Apache Drill is now available.

Dremel by Google was the inspiration for Apache drill. When you select columns to retrieve, it performs well. Multiple data sources can be joined together (e.g. join over hive and MongoDB, join over RDBMS and MongoDB, etc.)

Also, pure MongoDB or MongoDB + Apache Drill are both viable options.


Stick to native MongoDB if your application architecture is entirely based on MongoDB. You have access to all of MongoDB's features. MongoDB java driver, python driver, REST API, and other options are available. Yes, learning MongoDB-specific concepts will take more time. However, RDBMS queries provide you a lot of flexibility, and you can do a lot of things over here.

MongoDB + Apache Drill

You can choose this option if you can accomplish your goal with JPA or SQL queries and you are more familiar with RDBMS queries.

Additional benefit: You can use dig to query across additional data sources such as hive/HDFS or RDBMS in addition to MongoDB in the future.


Ques. 18): What is an example of a real-time use of Apache Drill? What makes Drill superior to Hive?


Hive is a batch processing framework that is best suited for processes that take a long time to complete. Drill outperforms Hive when it comes to data exploration and business intelligence.

Drill is also not exclusive to Hadoop. It can, for example, query NoSQL databases (such as MongoDB and HBase) and cloud storage (eg, Amazon S3, Google Cloud Storage, Azure Blob Storage, Swift).


Ques. 19): Is Cloudera Impala similar to the Apache Drill incubator project?


It's difficult to make a fair comparison because both initiatives are still in the early stages. We still have a lot of work to do because the Apache Drill project was only started a few months ago. That said, I believe it is critical to discuss some of the Apache Drill project's techniques and goals, which are critical to comprehend when comparing the two:

  • Apache Drill is a community-driven product run under the Apache foundation, with all the benefits and guarantees it entails.
  • Apache Drill committers are scattered across many different companies.

Apache Drill is a NoHadoop (not just Hadoop) project with the goal of providing distributed query capabilities across a variety of large data systems, including MongoDB, Cassandra, Riak, and Splunk.

  • By supporting all major Hadoop distributions, including Apache, Hortonworks, Cloudera, and MapR, Apache Drill avoids vendor lock-in.
  • Apache Drill allows you to do queries on hierarchical data.
  • JSON and other schemaless data are supported by Apache Drill.
  • The Apache Drill architecture is built to make third-party and custom integrations as simple as possible by clearly specifying interfaces for query languages, query optimizers, storage engines, user-defined functions, user-defined nested data functions, and so on.

Clearly, the Apache Drill project has a lot to offer and a lot of qualities. These things are only achievable because of the enormous amount of effort and interest that a big number of firms have begun to contribute to the project, which is only possible because of the Apache umbrella's power.


Ques. 20): Why is MapR mentioning Apache Drill so much?


Originally Answered: Why is MapR mentioning Apache Drill so much?

Drill is a new and interesting low latency SQL-on-Hadoop solution with more functionality than the other options available, and MapR has done it in the Apache Foundation so that it, like Hive, is a real community shared open source project, which means it's more likely to gain wider adoption.

Drill is MapR's baby, so they're right to be proud of it - it's the most exciting thing to happen to SQL-on-Hadoop in years. They're also discussing it since it addresses real-world problems and advances the field.

Consider Drill to be what Impala could have been if it had more functionality and was part of the Apache Foundation.