H13-711_V3.0 Questions and Answers

The RegionServer is generally deployed with the NameNode of the HDFS cluster to realize the data storage function.

Data processing and computing unit of HBase

Regionserver is the data service process of HBase, responsible for processing user data read and write requests

Manage all Regions, Regions can be migrated between RegionServers

Collect data on data sources such as Thrift, Avro, Syslog, Kafka, etC.

Local file data collection

Collection of logs generated by the application system

Real-time data collection of large amounts of data

Data transfer between Flume cascade nodes supports encryption

Flume supports multi-cascading and multiplexing

There is a need for encryption inside processes such as Source to Channel to Sink

Data transfer between Flume cascade nodes does not support compression

Python

Java

language

Scala

High reliability

high performance

safety

Graphical

Below 30 nodes

More than 100 nodes

100-200 nodes

More than 200 nodes

1.2, 2

1, 3, 2

2, 3, 1

2, 3, 3

File

Kafka

Memory

HDFS

Randomly selected

Adjudicated by RegionServer

Adjudication via Zookeeper

HMaster is a dual-master mode and does not need to be adjudicated

Perform Hive DDL operations

Run MapReduce tasks

Run Hive HQL task

everything aboveA. True

All 4 blocks will be written to RAM_DISK

If it is a copy other than the first copy that fails to write, try to write to the storage type specified by "Alternative storage policy for copy"

The first block copy will be written to RAM_DISK, the rest of the replicas are written to DISK

If the first block copy is written to RAM_If DISK fails, try to write to the storage type specified by "Alternative Storage Policy"

input settings

convert

output

Basic Information

spark streaming

spark core

mapreduce

spark sql

In the memory of the JobManager

In a single-machine database with high reliability

in the local file system

in HDFS

name

connect

type

priority

CAS

AOS

Kerberos

LDAP

Standby NameNode

DataNode

Active NameNodeA

job status

Job start/run time

Error lines/number of files

dirtydata link

string

Sat

Hash

sorted set

True

False

supportTez, Spark and other computing engines

Can query and manage petabytes of distributed data

E for dataTL process automation

Direct access to HDFS files and HBase

The disk configuration used to store Kafka data (such as the number of disks, size, etC. ) cannot meet the current business data stream declaration, resulting in the disk usage reaching the upper limit

The data storage time is configured too long, and the accumulated data reaches the upper limit of the disk usage.

Unreasonable business planning results in uneven data distribution and makes some disks reach the upper limit of usage

Caused by the failure of the rocker node

Updates sent by the client are applied in the order in which they were sent

A message is to be received by more than half-respected servers,he will be able to successfully write to disk

A message update can only succeed or fail. There is no intermediate state

The number of Zookeeper nodes must be odd

Distributed detection mechanism

ACK

Watcher

Checkpoint

Zookeeper

HDFS

Spark

HBase

bin/kafka-topiC. sh-create

bin/kafka-topiC. sh -list

bin/kafka-topiC. sh -describe

bin/kafka-topiC. sh -delete

Internal Operations Management

Supply Chain Management

Customer Analysis

Marketing Analysis

The expansion of nodes and disk services is not interrupted, and the update thesaurus service is not interrupted.

Compatible with Elasticsearch APIs

Does not support the fusion search of rich text and text such as voice

Level data retrieval returns in seconds, billion-level 100-dimensional star retrieval returns in seconds

Reduced key-values

Turn off persistence

Limit Redis memory size

slowlog configuration

DataNode A

DataNode B

DataNode E

DataNode F

Complete the hardware installation

Complete the node host OS installation

Prepare tools and software. Such as Putty, LLD, Fusioninsight HD software installation package, etC.

Prepare planning data. such as network parameters and role deployment locations

Real-time online data analysis

Non-real-time analysis, such as log analysis, statistical analysis

Data mining, such as user behavior analysis, interest division, regional display

Data aggregation, such as daily, weekly user clicks,Click to rank

Supports threshold control for the total number of established connections

Supports threshold control for the number of connections established by each user

Supports threshold control on the number of connections established by a specific user

Supports threshold control of the number of connections established per unit time

High reliability

high performance

column oriented

adjustable

One-stop management of tenant resources, computing resources (CPU/memory/IO), storage resources (HDFS), service resources (HBase.…)

Visual multi-level tenant management, matching with the enterprise organizational structure, simplifies system resource allocation and management

Tenant resource isolation based on container mechanism, escorting tenant SLA

A multi-level tenant model that matches the organizational structure of the enterprise, different departments correspond to different tenants, and tenants can be dynamically added or deleted as needed

Download logs of all components of installed components

Download logs for a module of a single component

Download logs for the specified host

Download logs for a specified time period

Before deploying Kerberos services, LD must be deployedAP service

The LDAP service must be deployed on the same node as the Kerberos service

Deploying the same node for Kerberos service and LDAP service facilitates data access and improves performance

LDAP services can be shared by multiple clusters

Visual modeling helps DBAs in modeling design and lowers the threshold for using HBase

Qualifier HBase column: each column represents an attribute of business data

Realize the division of labor: DBAs focus on data table modeling, developers focus on user table names and columns used

Column user table column: each column represents a KeyValue

FIFO scheduler

capacity scheduler

Fair scheduler none of the

Hadoop layer

GaussDB 200

Datafarm layer

Fusiolnght Manager

Region load balancing, Region splitting and Region allocation after splitting

Responsible for creating tables, modifying tables, deleting tables

Responsible for load balancing of RegionServer

Region after RegionServer failsmigrate

The Producer is responsible for producing data, and the Consumer is responsible for consuming data. A connection needs to be established between the Producer and the Consumer.

Proucer can send data to both Broker and Consumer

As a message producer, Proucer can directly write data to Zookeeper

Proucer can generate data by connecting to any normal Broker instance

time window

sliding window

session window

Cout window

Monitoring Alarm Threshold Profiles

Cluster installation template file

Configuration files for HDFS and YARN

Configuration file Check required to execute Precheck Nodes.Config

Unified Authentication Server

Unified authentication management module

Tingyi Security Management Module

Identity information storage server

During the upgrade process, you cannot manually perform the active/standby OMS switchover.

The root account password of all hosts in the cluster must be the same

Keep the network unblocked. Avoid abnormal upgrades due to network problems

No expansion can be done during the observation period

Spark SQL

Spark Streaming

Spark GraphX

Spark Mllib

create table

Import Data

subquery

J0IN query

4

3

2

1

UNION ALL

GROUP BY

SELECT

JOIN

Minimum queue level first

Resource recycling request queue priority

Maximum queue level first

The queue with the lowest resource utilization takes precedence

True

False

True

False

True

False

True

False

Processing time,eventquiltsystem processing time

eventime,the time when the event occurred

ingestion time, the time the event arrives at the stream processing system

delay time,Right nowthingincident to processingextendlate time

True

False

Fair

FIFO

Mesos

Capacity

True

False

TRUE

FALSE

True

False

True

False

trim()

abs ()

substr()

length()

True

False

True

False

The root of the Ldap Server directory tree generally defines the domain name the C?cin Compnitment

Ldap Server directory information is organized and stored based on a tree structure.

Each node in the Ldap Server directory tree is called an entry, and I have its own uniquely distinguishable name? ?(Plantnad? ?)

Specific objects cannot be defined under organizational units.

True

False

True

False

True

False

distributiondelivery mode

pollinghand overmodel

to sendcloth subscription model

point-to-point speedmoldMode

True

False

True

False

customerendis HDFSpart of isDeploy HDFSmustGrouppiece

HDFS guestThe client provides something likeshe 11'sOrderVisit by wayAsk HDFSnumber inaccording to

HDFSThe client is a library,IncludeHDFS filePartssysteminterface, thesecatchmouth hiddenHDFSMost of the complex in the implementationmiscellaneoussex

customerendcan support opening,Common operations such as read and write

True

False

quick

skillful

spirit

light

hadoop

head

bigdesk

IKAnalyzer

87

85

90

86

Manually clear warnings

Start and stop the service instance

delete service instance

Check history monitoring

Hive metadata storage is independent of data storage, thereby decoupling metadata and data. High flexibility, while traditional data warehouse data application is single, low flexibility

Hive is based on HDFS storage. Theoretically, the storage capacity can be expanded infinitely. The storage capacity of traditional data warehouses will have an upper limit

Since Hive data is stored in HDFS, it can ensure high fault tolerance and high reliability of data

Since Hive is based on a big data platform, query efficiency is faster than traditional data warehouses

Spark

host OS

YARN

HDFS

NameNode

Data Node

ZooKeeper

JobTaoker

Region

ColumnFamily

olumn

Row

surface

bucket

database

partition

all of the above

Offline computing

real-time interactive computing

Iterative calculation

Streaming Computing

warehouse

Metastore

State store

Storage

Kafka strongly depends on Zookeeper

The number of instances deployed by Kafka must not be less than 2

Kafkal server can generate messages

Consumer consumes messages as the client role of Kafkal

3

2

4

1

Configure how jobs connect to external data sources

Configure how jobs connect to internal data sources

Provide optimization parameters to improve data import and export performance

Make sure there are conversion steps

Support data insert, update and delete

long-term stability test

Stable and high performance

SQL Compatibility

everything aboveA. True

business model discovery

value for evaluation

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