前言:
<<line>> 表明在vertica 8.0文档中的title
 
正文:
1-支持平台
1.1-操作系统
<<Vertica Server and Vertica Management Console>>
When there are multiple minor versions supported for a major operating system release, Hewlett Packard Enterprise recommends that you run Vertica on the latest minor version listed in the supported versions list. For example, if you run Vertica on a Red Hat Enterprise Linux 6.x release, Hewlett Packard Enterprise recommends you upgrade to or be running the latest supported RHEL 6.x release, which is 6.8.
官方推荐rhel6.8或centos6.8版本
 
1.2- 文件系统
Vertica Analytic Database Enterprise Edition has been tested on all supported Linux platforms running the ext4 file system. For the Vertica Analytic Database I/O profile, the ext4 file system is considerably faster than ext3.
官方推荐ext4方式,不要使用LVM
 
1.3-hadoop支持
<<Vertica Integrations for Hadoop>>
支持CDH 5.6-5.8,其中5.6逐渐不再支持
 
1.4-kafka支持
<<Vertica Integration for Apache Kafka>>
支持Kafka 0.8.x-0.9,其中0.8.x逐渐不再支持
 
1.5-java/R支持
jdk 1.6-1.8
R语言3.0
 
2-新特性-
2.1-机器学习的资源池单独设置
<<New Resource Pool for BLOBDATA>>
blobdata资源池的设置
 
2.2-异常值检测
outliner过程
<<DETECT_OUTLIERS>>
基于标准差(待定是不是标准差)来筛选数据
If data point > ( value * STDDEV( data ) ) then data point is an outlier.
dbadmin=> SELECT * FROM baseball_roster;
id | last_name | hr | avg
----+-----------+--------+--------
1 | Polo | 7 | 0.233
2 | Gloss | 45 | 0.17
3 | Gus | 12 | 0.345
4 | Gee | 1 | 0.125
5 | Laus | 3 | 0.095
6 | Hilltop | 16 | 0.222
7 | Wicker | 78 | 0.333
8 | Scooter | 0 | 0.121
9 | Hank | 999999 | 0.8888
10 | Popup | 35 | 0.378
(10 rows)
 
dbadmin=> SELECT * FROM baseball_outliers;
id | last_name | hr | avg
----+-----------+--------+--------
7 | Wicker | 78 | 0.333
9 | Hank | 999999 | 0.8888
(2 rows)
 
2.3-机器学习相关
2.3.1-期初导入
<<Downloading the Machine Learning Example Data>>
yum install git
vsql -d dbname -f load_ml_data.sql
 
2.3.2-训练模型
<<NAIVE_BAYES>>
#训练基础表,党派人士的投票倾向
select * from public.house84_train;
#训练model//训练基础表//要预测哪一列//基于那些数据来预测//排除非数据列
SELECT NAIVE_BAYES
('naive_house84_model', 'house84_train', 'party', '*','--exclude_columns="party, id"');
 
2.3.3-验证模型
<<Classifying Data Using Naive Bayes>>
SELECT SUMMARIZE_MODEL('naive_house84_model', 'dbadmin');
基于模型来检验是民主党还是共和党
CREATE TABLE predicted_party_naive
AS SELECT party,
PREDICT_NAIVE_BAYES (vote1, vote2, vote3, vote4, vote5,
vote6, vote7, vote8, vote9, vote10, vote11, vote12, vote13, vote14,vote15, vote16
USING PARAMETERS model_name = 'naive_house84_model',owner = 'dbadmin',type = 'response') AS Predicted_Party
FROM house84_test;
 
 
每个预测的可能性百分比
SELECT PREDICT_NAIVE_BAYES_CLASSES (id, vote1, vote2, vote3, vote4, vote5,
vote6, vote7, vote8, vote9, vote10,
vote11, vote12, vote13, vote14,
vote15, vote16
USING PARAMETERS model_name = 'naive_house84_model',
owner = 'dbadmin',
key_columns = 'id', exclude_columns = 'id',
classes = 'democrat, republican')
OVER() FROM house84_test;
 
基于训练集的前三个vote,来预测党派
SELECT party, PREDICT_NAIVE_BAYES (vote1, vote2, vote3
USING PARAMETERS model_name = 'naive_house84_model',
owner = 'dbadmin',
type = 'response') AS Predicted_Party
FROM house84_test;