大数据复习案例

happybase操作Hbase语法：

import happybase
## 链接HBase数据库
conn = happybase.Connection(host=’localhost’, port=9090, timeout=None, autoconnect=True, table_prefix=None, table_prefix_separator=b’_’, compat=’0.98’, transport=’buffered’, protocol=’binary’)
## 创建表
conn.create_table(
	'shop',
	{
		'interfaceInfo' :dict(max_version=4),
		'inputInfo' :dict(max_version = 4)
	}
)
#插入
table = conn.table("shop")
with table.batch() as bat:
	bat.put('0001',{'interfaceInfo:inter_show':'HDM1', 'interfaseInfo:inter_network':'10Mbps', 'interfaceInfo:inter_three':'1个','interfaceInfo:inter_Type-c':'1个'})
	bat.put('0001',{'inputInfo:input_one':'有指点杆','inputInfo:input_tow':'全尺寸键盘','inputInfo:input_three':'多点触控','inputInfo:input_four':'多点触控'})

MapReduce语法（变压器案例为例）

mapper.py

#! /usr/bin/python3
# mapper处理之后的结果会自动按照key排序
import sys

def mapper(line):
    key = float(line.split(",")[2])

    cat = ''        
    if key <= 630.00:
        cat = "mini"
    elif key <= 6300:
        cat = "mid"
    else:
        cat = "over"
    print("%s\t%s" % (cat, 1))



def main():
    for line in sys.stdin:
        line = line.strip()
        if line.startswith('child'):
        	break
        else:
            mapper(line)

if __name__ == '__main__':
    main()

reduce.py

#! /usr/bin/python3

import sys

def reducer(k, values):
    print("%s:\t:%s" % (k, sum(values)))

def main():
    current_key = None
    values = []
    akey, avalue = None, None

    for line in sys.stdin:
        line = line.strip()
      
        try:
            akey, avalue = line.split('\t')
        except:
            continue
        if current_key == akey:
            values.append(int(avalue))
        else:
            if current_key:
                reducer(current_key, values)
                values = []
            values.append(int(avalue))
            current_key = akey
    if current_key == akey:
        reducer(current_key, values)

if __name__ == '__main__':
    main()

Spark语法

两种方式创建rdd

# 1.初始化 SparkContext，该对象是 Spark 程序的入口，‘Simple App’是程序的名字，一般自定义要通俗易懂
    sc = SparkContext("local", "Simple App")
# 如果是使用自己写入的文件的话，就用parallelize创建rdd
# 2.创建一个1到5的列表List
    data = [i for i in range(1,6)]

# 3.通过 SparkContext 并行化创建 rdd
    rdd = sc.parallelize(data)
# 如果是使用外部文件创建rdd的话，按照如下语句
   rdd = textFile("/root/wordcount.txt")

接着就是用所需要的算子来完成任务

算子部分edu的网址如下：
https://www.educoder.net/shixuns/imf67y2q/challenges

来源：oschina

链接：https://my.oschina.net/u/4330619/blog/4836503