mouthHBase

mouthHive

mouthSpark

mouthRedis

HDFSDepend onNameNode,DataNode,Clientcomposition.

HDFSprepareNameNodeThe metadata on theNameNodeSync the past directly.

HDFSThe first copy of the data can be stored by using the rack node close to the client.

HDFSIt is suitable for the scenario of writing once and reading many times.

existsorCreate an index on the database, save the data, and return all the data when the full text is checked.

existHBasestore data on theHBaseThe filtering characteristics of , satisfy fuzzy matching query.

When storing data, inSolrOn the construction of information, through the customs messenger is on the electricity, if the lack of quality, Wan Mo dad will put the complete data,

When querying, bysorFull-text search to obtain record key

True

False

Job

Context

FileSystem

Configuration

mouthIbClient.setRequestWriter(new BinaryRequestWriterO);

mouthif(updateRequest.getDocumentsMapO.size) > =10000){

cloudSolrClient.request(updateRequest,collName);updateRequest.clear);

mouthif(updateRequest.getDocuments).size(O > =1000){

cloudSolrClient.request(updateRequest,collName);updateRequest.clearO;

S8tW9H2xgntdc=new SolrInputDocument);

doc.addField( " id " ,i.tostring( )doc.addField( " name " , " Zhang San4i);

doc.addField( " features " , " test " +i):

doc.addreldc " price, (float)*1.oi);

cloudSolrClient.add(doc);

cloudSolrClient.commitO:

Nimbus

Supervisor

Broker

quorumpeer

True

False

HBaseIt can store massive data and support dynamic expansion, which can fulfill the storage requirements of logistics and sales data.

HbaseIt supports efficient random reading, and can complete real-time analysis and command of the logistics situation through reasonable design.

Logistics data is very sparse,HBaseCan handle sparse data efficiently.

HBaseIt can be built on ordinary commercial servers, and the construction cost is relatively low.

broadcast variable in everyTasksave a copy

broadcast variable in eachExecutorsave a copy

Broadcast variables are read-only

broadcast variable viaSparkContext.broadcast() method to generate

mergemapThe result of the stage has the samekeyvalue key-value pair

put allmapThe results of the stages are merged to get the final result

rightmapThe results of the stages are sorted and merged

Divide the input data into one by one. and split this further into pairs, then asmapfunction input of

Name+Phone number

date+Phone number

Phone number+date

Phone number+Name

True

False

INCR(increment the stored digital value by one)

HINCRBY(for the hash tablekeydomain infieldvalue plus incrementincrement)

LPUSH(for linked listspushoperate)

All of the above

Build queuing systems, such as message queues

uniqOperations, such as getting the ranking value of all data in a certain period of time

get latestNdata operations: for example, for a certain microblog, get the latest10comments

Simulate stack operations