Anil  Sakhiya

Anil Sakhiya

1595141479

Apache Spark For Beginners In 3 Hours | Apache Spark Training

In this Apache Spark For Beginners, we will have an overview of Spark in Big Data. We will start with an introduction to Apache Spark Programming. Then we will move to know the Spark History. Moreover, we will learn why Spark is needed and covers everything that an individual needed to master its skill in this field. In this Apache Spark tutorial, you will not only learn Spark from the basics but also through this Apache Spark tutorial, you will get to know the Spark architecture and its components such as Spark Core, Spark Programming, Spark SQL, Spark Streaming, and much more.

This “Spark Tutorial” will help you to comprehensively learn all the concepts of Apache Spark. Apache Spark has a bright future. Many companies have recognized the power of Spark and quickly started worked on it. The primary importance of Apache Spark in the Big data industry is because of its in-memory data processing. Spark can also handle many analytics challenges because of its low-latency in-memory data processing capability.

Spark’s shell provides you a simple way to learn the API, as well as a powerful tool to analyze data interactively. It is available in either Scala (which runs on the Java VM and is thus a good way to use existing Java libraries) or Python

This Spark tutorial will comprise of the following topics:

  • 00:00:00 - Introduction
  • 00:00:52 - Spark Fundamentals
  • 00:23:11 - Spark Architecture
  • 01:01:08 - Spark Demo

#apache-spark #apache #spark #big-data #developer

What is GEEK

Buddha Community

Apache Spark For Beginners In 3 Hours | Apache Spark Training
Anil  Sakhiya

Anil Sakhiya

1595141479

Apache Spark For Beginners In 3 Hours | Apache Spark Training

In this Apache Spark For Beginners, we will have an overview of Spark in Big Data. We will start with an introduction to Apache Spark Programming. Then we will move to know the Spark History. Moreover, we will learn why Spark is needed and covers everything that an individual needed to master its skill in this field. In this Apache Spark tutorial, you will not only learn Spark from the basics but also through this Apache Spark tutorial, you will get to know the Spark architecture and its components such as Spark Core, Spark Programming, Spark SQL, Spark Streaming, and much more.

This “Spark Tutorial” will help you to comprehensively learn all the concepts of Apache Spark. Apache Spark has a bright future. Many companies have recognized the power of Spark and quickly started worked on it. The primary importance of Apache Spark in the Big data industry is because of its in-memory data processing. Spark can also handle many analytics challenges because of its low-latency in-memory data processing capability.

Spark’s shell provides you a simple way to learn the API, as well as a powerful tool to analyze data interactively. It is available in either Scala (which runs on the Java VM and is thus a good way to use existing Java libraries) or Python

This Spark tutorial will comprise of the following topics:

  • 00:00:00 - Introduction
  • 00:00:52 - Spark Fundamentals
  • 00:23:11 - Spark Architecture
  • 01:01:08 - Spark Demo

#apache-spark #apache #spark #big-data #developer

A Wrapper for Sembast and SQFlite to Enable Easy

FHIR_DB

This is really just a wrapper around Sembast_SQFLite - so all of the heavy lifting was done by Alex Tekartik. I highly recommend that if you have any questions about working with this package that you take a look at Sembast. He's also just a super nice guy, and even answered a question for me when I was deciding which sembast version to use. As usual, ResoCoder also has a good tutorial.

I have an interest in low-resource settings and thus a specific reason to be able to store data offline. To encourage this use, there are a number of other packages I have created based around the data format FHIR. FHIR® is the registered trademark of HL7 and is used with the permission of HL7. Use of the FHIR trademark does not constitute endorsement of this product by HL7.

Using the Db

So, while not absolutely necessary, I highly recommend that you use some sort of interface class. This adds the benefit of more easily handling errors, plus if you change to a different database in the future, you don't have to change the rest of your app, just the interface.

I've used something like this in my projects:

class IFhirDb {
  IFhirDb();
  final ResourceDao resourceDao = ResourceDao();

  Future<Either<DbFailure, Resource>> save(Resource resource) async {
    Resource resultResource;
    try {
      resultResource = await resourceDao.save(resource);
    } catch (error) {
      return left(DbFailure.unableToSave(error: error.toString()));
    }
    return right(resultResource);
  }

  Future<Either<DbFailure, List<Resource>>> returnListOfSingleResourceType(
      String resourceType) async {
    List<Resource> resultList;
    try {
      resultList =
          await resourceDao.getAllSortedById(resourceType: resourceType);
    } catch (error) {
      return left(DbFailure.unableToObtainList(error: error.toString()));
    }
    return right(resultList);
  }

  Future<Either<DbFailure, List<Resource>>> searchFunction(
      String resourceType, String searchString, String reference) async {
    List<Resource> resultList;
    try {
      resultList =
          await resourceDao.searchFor(resourceType, searchString, reference);
    } catch (error) {
      return left(DbFailure.unableToObtainList(error: error.toString()));
    }
    return right(resultList);
  }
}

I like this because in case there's an i/o error or something, it won't crash your app. Then, you can call this interface in your app like the following:

final patient = Patient(
    resourceType: 'Patient',
    name: [HumanName(text: 'New Patient Name')],
    birthDate: Date(DateTime.now()),
);

final saveResult = await IFhirDb().save(patient);

This will save your newly created patient to the locally embedded database.

IMPORTANT: this database will expect that all previously created resources have an id. When you save a resource, it will check to see if that resource type has already been stored. (Each resource type is saved in it's own store in the database). It will then check if there is an ID. If there's no ID, it will create a new one for that resource (along with metadata on version number and creation time). It will save it, and return the resource. If it already has an ID, it will copy the the old version of the resource into a _history store. It will then update the metadata of the new resource and save that version into the appropriate store for that resource. If, for instance, we have a previously created patient:

{
    "resourceType": "Patient",
    "id": "fhirfli-294057507-6811107",
    "meta": {
        "versionId": "1",
        "lastUpdated": "2020-10-16T19:41:28.054369Z"
    },
    "name": [
        {
            "given": ["New"],
            "family": "Patient"
        }
    ],
    "birthDate": "2020-10-16"
}

And we update the last name to 'Provider'. The above version of the patient will be kept in _history, while in the 'Patient' store in the db, we will have the updated version:

{
    "resourceType": "Patient",
    "id": "fhirfli-294057507-6811107",
    "meta": {
        "versionId": "2",
        "lastUpdated": "2020-10-16T19:45:07.316698Z"
    },
    "name": [
        {
            "given": ["New"],
            "family": "Provider"
        }
    ],
    "birthDate": "2020-10-16"
}

This way we can keep track of all previous version of all resources (which is obviously important in medicine).

For most of the interactions (saving, deleting, etc), they work the way you'd expect. The only difference is search. Because Sembast is NoSQL, we can search on any of the fields in a resource. If in our interface class, we have the following function:

  Future<Either<DbFailure, List<Resource>>> searchFunction(
      String resourceType, String searchString, String reference) async {
    List<Resource> resultList;
    try {
      resultList =
          await resourceDao.searchFor(resourceType, searchString, reference);
    } catch (error) {
      return left(DbFailure.unableToObtainList(error: error.toString()));
    }
    return right(resultList);
  }

You can search for all immunizations of a certain patient:

searchFunction(
        'Immunization', 'patient.reference', 'Patient/$patientId');

This function will search through all entries in the 'Immunization' store. It will look at all 'patient.reference' fields, and return any that match 'Patient/$patientId'.

The last thing I'll mention is that this is a password protected db, using AES-256 encryption (although it can also use Salsa20). Anytime you use the db, you have the option of using a password for encryption/decryption. Remember, if you setup the database using encryption, you will only be able to access it using that same password. When you're ready to change the password, you will need to call the update password function. If we again assume we created a change password method in our interface, it might look something like this:

class IFhirDb {
  IFhirDb();
  final ResourceDao resourceDao = ResourceDao();
  ...
    Future<Either<DbFailure, Unit>> updatePassword(String oldPassword, String newPassword) async {
    try {
      await resourceDao.updatePw(oldPassword, newPassword);
    } catch (error) {
      return left(DbFailure.unableToUpdatePassword(error: error.toString()));
    }
    return right(Unit);
  }

You don't have to use a password, and in that case, it will save the db file as plain text. If you want to add a password later, it will encrypt it at that time.

General Store

After using this for a while in an app, I've realized that it needs to be able to store data apart from just FHIR resources, at least on occasion. For this, I've added a second class for all versions of the database called GeneralDao. This is similar to the ResourceDao, but fewer options. So, in order to save something, it would look like this:

await GeneralDao().save('password', {'new':'map'});
await GeneralDao().save('password', {'new':'map'}, 'key');

The difference between these two options is that the first one will generate a key for the map being stored, while the second will store the map using the key provided. Both will return the key after successfully storing the map.

Other functions available include:

// deletes everything in the general store
await GeneralDao().deleteAllGeneral('password'); 

// delete specific entry
await GeneralDao().delete('password','key'); 

// returns map with that key
await GeneralDao().find('password', 'key'); 

FHIR® is a registered trademark of Health Level Seven International (HL7) and its use does not constitute an endorsement of products by HL7®

Use this package as a library

Depend on it

Run this command:

With Flutter:

 $ flutter pub add fhir_db

This will add a line like this to your package's pubspec.yaml (and run an implicit flutter pub get):

dependencies:
  fhir_db: ^0.4.3

Alternatively, your editor might support or flutter pub get. Check the docs for your editor to learn more.

Import it

Now in your Dart code, you can use:

import 'package:fhir_db/dstu2.dart';
import 'package:fhir_db/dstu2/fhir_db.dart';
import 'package:fhir_db/dstu2/general_dao.dart';
import 'package:fhir_db/dstu2/resource_dao.dart';
import 'package:fhir_db/encrypt/aes.dart';
import 'package:fhir_db/encrypt/salsa.dart';
import 'package:fhir_db/r4.dart';
import 'package:fhir_db/r4/fhir_db.dart';
import 'package:fhir_db/r4/general_dao.dart';
import 'package:fhir_db/r4/resource_dao.dart';
import 'package:fhir_db/r5.dart';
import 'package:fhir_db/r5/fhir_db.dart';
import 'package:fhir_db/r5/general_dao.dart';
import 'package:fhir_db/r5/resource_dao.dart';
import 'package:fhir_db/stu3.dart';
import 'package:fhir_db/stu3/fhir_db.dart';
import 'package:fhir_db/stu3/general_dao.dart';
import 'package:fhir_db/stu3/resource_dao.dart'; 

example/lib/main.dart

import 'package:fhir/r4.dart';
import 'package:fhir_db/r4.dart';
import 'package:flutter/material.dart';
import 'package:test/test.dart';

Future<void> main() async {
  WidgetsFlutterBinding.ensureInitialized();

  final resourceDao = ResourceDao();

  // await resourceDao.updatePw('newPw', null);
  await resourceDao.deleteAllResources(null);

  group('Playing with passwords', () {
    test('Playing with Passwords', () async {
      final patient = Patient(id: Id('1'));

      final saved = await resourceDao.save(null, patient);

      await resourceDao.updatePw(null, 'newPw');
      final search1 = await resourceDao.find('newPw',
          resourceType: R4ResourceType.Patient, id: Id('1'));
      expect(saved, search1[0]);

      await resourceDao.updatePw('newPw', 'newerPw');
      final search2 = await resourceDao.find('newerPw',
          resourceType: R4ResourceType.Patient, id: Id('1'));
      expect(saved, search2[0]);

      await resourceDao.updatePw('newerPw', null);
      final search3 = await resourceDao.find(null,
          resourceType: R4ResourceType.Patient, id: Id('1'));
      expect(saved, search3[0]);

      await resourceDao.deleteAllResources(null);
    });
  });

  final id = Id('12345');
  group('Saving Things:', () {
    test('Save Patient', () async {
      final humanName = HumanName(family: 'Atreides', given: ['Duke']);
      final patient = Patient(id: id, name: [humanName]);
      final saved = await resourceDao.save(null, patient);

      expect(saved.id, id);

      expect((saved as Patient).name?[0], humanName);
    });

    test('Save Organization', () async {
      final organization = Organization(id: id, name: 'FhirFli');
      final saved = await resourceDao.save(null, organization);

      expect(saved.id, id);

      expect((saved as Organization).name, 'FhirFli');
    });

    test('Save Observation1', () async {
      final observation1 = Observation(
        id: Id('obs1'),
        code: CodeableConcept(text: 'Observation #1'),
        effectiveDateTime: FhirDateTime(DateTime(1981, 09, 18)),
      );
      final saved = await resourceDao.save(null, observation1);

      expect(saved.id, Id('obs1'));

      expect((saved as Observation).code.text, 'Observation #1');
    });

    test('Save Observation1 Again', () async {
      final observation1 = Observation(
          id: Id('obs1'),
          code: CodeableConcept(text: 'Observation #1 - Updated'));
      final saved = await resourceDao.save(null, observation1);

      expect(saved.id, Id('obs1'));

      expect((saved as Observation).code.text, 'Observation #1 - Updated');

      expect(saved.meta?.versionId, Id('2'));
    });

    test('Save Observation2', () async {
      final observation2 = Observation(
        id: Id('obs2'),
        code: CodeableConcept(text: 'Observation #2'),
        effectiveDateTime: FhirDateTime(DateTime(1981, 09, 18)),
      );
      final saved = await resourceDao.save(null, observation2);

      expect(saved.id, Id('obs2'));

      expect((saved as Observation).code.text, 'Observation #2');
    });

    test('Save Observation3', () async {
      final observation3 = Observation(
        id: Id('obs3'),
        code: CodeableConcept(text: 'Observation #3'),
        effectiveDateTime: FhirDateTime(DateTime(1981, 09, 18)),
      );
      final saved = await resourceDao.save(null, observation3);

      expect(saved.id, Id('obs3'));

      expect((saved as Observation).code.text, 'Observation #3');
    });
  });

  group('Finding Things:', () {
    test('Find 1st Patient', () async {
      final search = await resourceDao.find(null,
          resourceType: R4ResourceType.Patient, id: id);
      final humanName = HumanName(family: 'Atreides', given: ['Duke']);

      expect(search.length, 1);

      expect((search[0] as Patient).name?[0], humanName);
    });

    test('Find 3rd Observation', () async {
      final search = await resourceDao.find(null,
          resourceType: R4ResourceType.Observation, id: Id('obs3'));

      expect(search.length, 1);

      expect(search[0].id, Id('obs3'));

      expect((search[0] as Observation).code.text, 'Observation #3');
    });

    test('Find All Observations', () async {
      final search = await resourceDao.getResourceType(
        null,
        resourceTypes: [R4ResourceType.Observation],
      );

      expect(search.length, 3);

      final idList = [];
      for (final obs in search) {
        idList.add(obs.id.toString());
      }

      expect(idList.contains('obs1'), true);

      expect(idList.contains('obs2'), true);

      expect(idList.contains('obs3'), true);
    });

    test('Find All (non-historical) Resources', () async {
      final search = await resourceDao.getAll(null);

      expect(search.length, 5);
      final patList = search.toList();
      final orgList = search.toList();
      final obsList = search.toList();
      patList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Patient);
      orgList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Organization);
      obsList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Observation);

      expect(patList.length, 1);

      expect(orgList.length, 1);

      expect(obsList.length, 3);
    });
  });

  group('Deleting Things:', () {
    test('Delete 2nd Observation', () async {
      await resourceDao.delete(
          null, null, R4ResourceType.Observation, Id('obs2'), null, null);

      final search = await resourceDao.getResourceType(
        null,
        resourceTypes: [R4ResourceType.Observation],
      );

      expect(search.length, 2);

      final idList = [];
      for (final obs in search) {
        idList.add(obs.id.toString());
      }

      expect(idList.contains('obs1'), true);

      expect(idList.contains('obs2'), false);

      expect(idList.contains('obs3'), true);
    });

    test('Delete All Observations', () async {
      await resourceDao.deleteSingleType(null,
          resourceType: R4ResourceType.Observation);

      final search = await resourceDao.getAll(null);

      expect(search.length, 2);

      final patList = search.toList();
      final orgList = search.toList();
      patList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Patient);
      orgList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Organization);

      expect(patList.length, 1);

      expect(patList.length, 1);
    });

    test('Delete All Resources', () async {
      await resourceDao.deleteAllResources(null);

      final search = await resourceDao.getAll(null);

      expect(search.length, 0);
    });
  });

  group('Password - Saving Things:', () {
    test('Save Patient', () async {
      await resourceDao.updatePw(null, 'newPw');
      final humanName = HumanName(family: 'Atreides', given: ['Duke']);
      final patient = Patient(id: id, name: [humanName]);
      final saved = await resourceDao.save('newPw', patient);

      expect(saved.id, id);

      expect((saved as Patient).name?[0], humanName);
    });

    test('Save Organization', () async {
      final organization = Organization(id: id, name: 'FhirFli');
      final saved = await resourceDao.save('newPw', organization);

      expect(saved.id, id);

      expect((saved as Organization).name, 'FhirFli');
    });

    test('Save Observation1', () async {
      final observation1 = Observation(
        id: Id('obs1'),
        code: CodeableConcept(text: 'Observation #1'),
        effectiveDateTime: FhirDateTime(DateTime(1981, 09, 18)),
      );
      final saved = await resourceDao.save('newPw', observation1);

      expect(saved.id, Id('obs1'));

      expect((saved as Observation).code.text, 'Observation #1');
    });

    test('Save Observation1 Again', () async {
      final observation1 = Observation(
          id: Id('obs1'),
          code: CodeableConcept(text: 'Observation #1 - Updated'));
      final saved = await resourceDao.save('newPw', observation1);

      expect(saved.id, Id('obs1'));

      expect((saved as Observation).code.text, 'Observation #1 - Updated');

      expect(saved.meta?.versionId, Id('2'));
    });

    test('Save Observation2', () async {
      final observation2 = Observation(
        id: Id('obs2'),
        code: CodeableConcept(text: 'Observation #2'),
        effectiveDateTime: FhirDateTime(DateTime(1981, 09, 18)),
      );
      final saved = await resourceDao.save('newPw', observation2);

      expect(saved.id, Id('obs2'));

      expect((saved as Observation).code.text, 'Observation #2');
    });

    test('Save Observation3', () async {
      final observation3 = Observation(
        id: Id('obs3'),
        code: CodeableConcept(text: 'Observation #3'),
        effectiveDateTime: FhirDateTime(DateTime(1981, 09, 18)),
      );
      final saved = await resourceDao.save('newPw', observation3);

      expect(saved.id, Id('obs3'));

      expect((saved as Observation).code.text, 'Observation #3');
    });
  });

  group('Password - Finding Things:', () {
    test('Find 1st Patient', () async {
      final search = await resourceDao.find('newPw',
          resourceType: R4ResourceType.Patient, id: id);
      final humanName = HumanName(family: 'Atreides', given: ['Duke']);

      expect(search.length, 1);

      expect((search[0] as Patient).name?[0], humanName);
    });

    test('Find 3rd Observation', () async {
      final search = await resourceDao.find('newPw',
          resourceType: R4ResourceType.Observation, id: Id('obs3'));

      expect(search.length, 1);

      expect(search[0].id, Id('obs3'));

      expect((search[0] as Observation).code.text, 'Observation #3');
    });

    test('Find All Observations', () async {
      final search = await resourceDao.getResourceType(
        'newPw',
        resourceTypes: [R4ResourceType.Observation],
      );

      expect(search.length, 3);

      final idList = [];
      for (final obs in search) {
        idList.add(obs.id.toString());
      }

      expect(idList.contains('obs1'), true);

      expect(idList.contains('obs2'), true);

      expect(idList.contains('obs3'), true);
    });

    test('Find All (non-historical) Resources', () async {
      final search = await resourceDao.getAll('newPw');

      expect(search.length, 5);
      final patList = search.toList();
      final orgList = search.toList();
      final obsList = search.toList();
      patList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Patient);
      orgList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Organization);
      obsList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Observation);

      expect(patList.length, 1);

      expect(orgList.length, 1);

      expect(obsList.length, 3);
    });
  });

  group('Password - Deleting Things:', () {
    test('Delete 2nd Observation', () async {
      await resourceDao.delete(
          'newPw', null, R4ResourceType.Observation, Id('obs2'), null, null);

      final search = await resourceDao.getResourceType(
        'newPw',
        resourceTypes: [R4ResourceType.Observation],
      );

      expect(search.length, 2);

      final idList = [];
      for (final obs in search) {
        idList.add(obs.id.toString());
      }

      expect(idList.contains('obs1'), true);

      expect(idList.contains('obs2'), false);

      expect(idList.contains('obs3'), true);
    });

    test('Delete All Observations', () async {
      await resourceDao.deleteSingleType('newPw',
          resourceType: R4ResourceType.Observation);

      final search = await resourceDao.getAll('newPw');

      expect(search.length, 2);

      final patList = search.toList();
      final orgList = search.toList();
      patList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Patient);
      orgList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Organization);

      expect(patList.length, 1);

      expect(patList.length, 1);
    });

    test('Delete All Resources', () async {
      await resourceDao.deleteAllResources('newPw');

      final search = await resourceDao.getAll('newPw');

      expect(search.length, 0);

      await resourceDao.updatePw('newPw', null);
    });
  });
} 

Download Details:

Author: MayJuun

Source Code: https://github.com/MayJuun/fhir/tree/main/fhir_db

#sqflite  #dart  #flutter 

kiran sam

1619408437

Apache Spark Training Course Online - Learn Scala

R is perhaps the most popular computer dialects in information science, explicitly committed to measurable investigation with a number of augmentations, for example, RStudio addins and other R packages, for information processing and machine learning assignments. Moreover, it empowers information researchers to effortlessly imagine their informational collection.

By using SparkR in Apache SparkTM, R code can without much of a stretch be scaled. To interactively run occupations, you can without much of a stretch run the distributed calculation by running a R shell.

At the point when SparkR doesn’t require interaction with the R process, the performance is virtually indistinguishable from other language APIs like Scala, Java and Python. However, huge performance degradation happens when SparkR occupations interact with local R capacities or information types.

Databricks Runtime introduced vectorization in SparkR to improve the performance of information I/O among Spark and R. We are eager to declare that using the R APIs from Apache Arrow 0.15.1, the vectorization is presently accessible in the upcoming Apache Spark 3.0 with the significant performance improvements.

This blog entry outlines Spark and R interaction inside SparkR, the current local execution and the vectorized execution in SparkR with benchmark results.Native implementation

The calculation on SparkR DataFrame gets distributed across every one of the hubs accessible on the Spark cluster. There’s no correspondence with the R processes above in driver or executor sides on the off chance that it doesn’t have to collect information as R data.frame or to execute R native capacities. At the point when it requires R data.frame or the execution of R native capacity, they convey using attachments among JVM and R driver/executors.

It (de)serializes and transfers information row by row among JVM and R with an inefficient encoding format, which doesn’t consider the modern CPU plan, for example, CPU pipelining.

Vectorized implementation

In Apache Spark 3.0, another vectorized implementation is introduced in SparkR by leveraging Apache Arrow to trade information directly among JVM and R driver/executors with minimal (de)serialization cost

Instead of (de)serializing the information row by row using an inefficient format among JVM and R, the new implementation leverages Apache Arrow to permit pipelining and Single Instruction Multiple Data (SIMD) with a productive columnar format.

The new vectorized SparkR APIs are not empowered as a matter of course yet can be empowered by setting spark.sql.execution.arrow.sparkr.enabled to true in the upcoming Apache Spark 3.0. Note that vectorized dapplyCollect() and gapplyCollect() are not executed at this point. It is encouraged for users to utilize dapply() and gapply() instead.

Benchmark results

The benchmarks were performed with a basic informational collection of 500,000 records by executing similar code and comparing the all out passed times when the vectorization is empowered and impaired.

If there should be an occurrence of collect() and createDataFrame() with R DataFrame, it turned out to be approximately 17x and 42x faster when the vectorization was empowered. For dapply() and gapply(), it was 43x and 33x faster than when the vectorization is handicapped, respectively.

There was a performance improvement of up to 17x–43x when the streamlining was empowered by spark.sql.execution.arrow.sparkr.enabled to true. The larger the information was, the higher performance anticipated

End

The upcoming Apache Spark 3.0, supports the vectorized APIs, dapply(), gapply(), collect() and createDataFrame() with R DataFrame by leveraging Apache Arrow. Enabling vectorization in SparkR improved the performance up to 43x faster, and more lift is normal when the size of information is larger.

Concerning future work, there is an ongoing issue in Apache Arrow, ARROW-4512. The correspondence among JVM and R isn’t completely in a streaming manner currently. It needs to (de)serialize in clump since Arrow R API doesn’t support this out of the container. Also, dapplyCollect() and gapplyCollect() will be supported in Apache Spark 3.x releases. Users can work around through dapply() and collect(), and gapply() and collect() individually in the interim.

Try out these new abilities today on Databricks, through our DBR 7.0 Beta, which includes a preview of the upcoming Spark 3.0 release. Learn more about Spark 3.0 in our Spark Certification

Spark and R interaction

SparkR supports not just a rich arrangement of ML and SQL-like APIs yet additionally a bunch of APIs normally used to directly interact with R code — for instance, the consistent conversion of Spark DataFrame from/to R DataFrame, and the execution of R local capacities on Spark DataFrame in a distributed manner.

In many cases, the performance is virtually steady across other language APIs in Spark — for instance, when user code relies on Spark UDFs or potentially SQL APIs, the execution happens entirely inside the JVM with no performance punishment in I/O. See the cases beneath which take ~1 second similarly.

/Scala API

/~1 second

sql(“SELECT id FROM range(2000000000)”).filter(“id > 10”).count()

R API

~1 second

count(filter(sql(“SELECT * FROM range(2000000000)”), “id > 10”))

However, in situations where it requires to execute the R local capacity or convert it from/to R local sorts, the performance is immensely different as underneath.

/Scala API

val ds = (1L to 100000L).toDS

/~1 second

ds.mapPartitions(iter => iter.filter(_ < 50000)).count()

R API

df <-createDataFrame(lapply(seq(100000), work (e) list(value=e)))

~15 seconds - multiple times slower

count(dapply(

df, function(x) as.data.frame(x[x$value < 50000,]), schema(df)))

Albeit this basic case above filters the qualities lower than 50,000 for each partition, SparkR is 15x slower.

/Scala API

/~0.2 seconds

val df = sql(“SELECT * FROM range(1000000)”).collect()

R API

~8 seconds - multiple times slower

df <-collect(sql(“SELECT * FROM range(1000000)”))

The case above is far and away more terrible. It just collects a similar information into the driver side, yet it is 40x slower in SparkR.

This is on the grounds that the APIs that require the interaction with R local capacity or information types and its execution are not very productive. There are six APIs that have the striking performance punishment:

createDataFrame()

collect()

dapply()

dapplyCollect()

gapply()

gapplyCollect()

In short, createDataFrame() and collect() require to (de)serialize and convert the information from JVM from/to R driver side. For instance, String in Java becomes character in R. For dapply() and gapply(), the conversion among JVM and R executors is required in light of the fact that it needs to (de)serialize both R local capacity and the information. If there should arise an occurrence of dapplyCollect() and gapplyCollect(), it requires the overhead at both driver and executors among JVM and R

Native implementation

The calculation on SparkR DataFrame gets distributed across every one of the hubs accessible on the Spark cluster. There’s no correspondence with the R processes above in driver or executor sides on the off chance that it doesn’t have to collect information as R data.frame or to execute R native capacities. At the point when it requires R data.frame or the execution of R native capacity, they convey using attachments among JVM and R driver/executors.

It (de)serializes and transfers information row by row among JVM and R with an inefficient encoding format, which doesn’t consider the modern CPU plan, for example, CPU pipelining.

Vectorized implementation

In Apache Spark 3.0, another vectorized implementation is introduced in SparkR by leveraging Apache Arrow to trade information directly among JVM and R driver/executors with minimal (de)serialization cost

Instead of (de)serializing the information row by row using an inefficient format among JVM and R, the new implementation leverages Apache Arrow to permit pipelining and Single Instruction Multiple Data (SIMD) with a productive columnar format.

The new vectorized SparkR APIs are not empowered as a matter of course yet can be empowered by setting spark.sql.execution.arrow.sparkr.enabled to true in the upcoming Apache Spark 3.0. Note that vectorized dapplyCollect() and gapplyCollect() are not executed at this point. It is encouraged for users to utilize dapply() and gapply() instead.

Benchmark results

The benchmarks were performed with a basic informational collection of 500,000 records by executing similar code and comparing the all out passed times when the vectorization is empowered and impaired.

If there should be an occurrence of collect() and createDataFrame() with R DataFrame, it turned out to be approximately 17x and 42x faster when the vectorization was empowered. For dapply() and gapply(), it was 43x and 33x faster than when the vectorization is handicapped, respectively.

There was a performance improvement of up to 17x–43x when the streamlining was empowered by spark.sql.execution.arrow.sparkr.enabled to true. The larger the information was, the higher performance anticipated

End

The upcoming Apache Spark 3.0, supports the vectorized APIs, dapply(), gapply(), collect() and createDataFrame() with R DataFrame by leveraging Apache Arrow. Enabling vectorization in SparkR improved the performance up to 43x faster, and more lift is normal when the size of information is larger.

Concerning future work, there is an ongoing issue in Apache Arrow, ARROW-4512. The correspondence among JVM and R isn’t completely in a streaming manner currently. It needs to (de)serialize in clump since Arrow R API doesn’t support this out of the container. Also, dapplyCollect() and gapplyCollect() will be supported in Apache Spark 3.x releases. Users can work around through dapply() and collect(), and gapply() and collect() individually in the interim.

Try out these new abilities today on Databricks, through our DBR 7.0 Beta, which includes a preview of the upcoming Spark 3.0 release.

#apache spark #apache spark training #apache spark course

Shreya kapoor

Shreya kapoor

1604946745

200 hour Yoga Teacher Training Course in Ghaziabad, India | Divyaa Yoga Institute

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About Divyaa yoga institute
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Edureka Fan

Edureka Fan

1606982795

What is Apache Spark? | Apache Spark Python | Spark Training

This Edureka “What is Apache Spark?” video will help you to understand the Architecture of Spark in depth. It includes an example where we Understand what is Python and Apache Spark.

#big-data #apache-spark #developer #apache #spark