MySQL同步数据至elasticsearch

  • 经过各种测试本文采用binlog方式
  • 中间件选用go_mysql_elasticsearch
  • 使用docker方式部署(镜像已经制作好了上传至阿里云)


    docker run -it -v /root/mysql.toml:/mysql-river-es5.toml -v /mysqlmaster/:/go/src/github.com/siddontang/go-mysql-elasticsearch/var registry.cn-hangzhou.aliyuncs.com/yanfulei/gomysqles5:5.5.3
docker run -it -v /root/mysql.toml:/mysql-river-es5.toml -v /mysqlmaster/:/go/src/github.com/siddontang/go-mysql-elasticsearch/var registry.cn-hangzhou.aliyuncs.com/yanfulei/gomysqles5:5.5.3
  • 挂载文件/root/mysql.toml为go_mysql_elasticsearch的配置文件,内容直接贴出
# MySQL address, user and password
# user must have replication privilege in MySQL.
my_addr = "xx.xx.xx.xxx:3306"
my_user = "root"
my_pass = "xxxxx"
my_charset = "utf8"

# Set true when elasticsearch use https
#es_https = false
# Elasticsearch address
es_addr = "xx.xx.xxx.xxxx:9200"
# Elasticsearch user and password, maybe set by shield, nginx, or x-pack
es_user = "elastic"
es_pass = "changeme"

# Path to store data, like master.info, if not set or empty,
# we must use this to support breakpoint resume syncing. 
# TODO: support other storage, like etcd. 
data_dir = "./var"

# Inner Http status address
stat_addr = "127.0.0.1:12800"

# pseudo server id like a slave 
server_id = 1001

# mysql or mariadb
flavor = "mysql"

# mysqldump execution path
# if not set or empty, ignore mysqldump.
mysqldump = "mysqldump"

# if we have no privilege to use mysqldump with --master-data,
# we must skip it.
#skip_master_data = false

# minimal items to be inserted in one bulk
bulk_size = 128

# force flush the pending requests if we don't have enough items >= bulk_size
flush_bulk_time = "200ms"

# Ignore table without primary key
skip_no_pk_table = false

# MySQL data source
[[source]]
schema = "smh_orders"

# Only below tables will be synced into Elasticsearch.
# "t_[0-9]{4}" is a wildcard table format, you can use it if you have many sub tables, like table_0000 - table_1023
# I don't think it is necessary to sync all tables in a database.
tables = ["cl_cashlog", "cl_cashlogdetails", "od_orderdiscount", "od_orderexpress", "od_orderitems", "od_orders", "py_paymethod", "py_payparam", "py_storepaymethod", "py_weapppayparam"]

# Below is for special rule mapping

# Very simple example
# 
# desc t;
# +-------+--------------+------+-----+---------+-------+
# | Field | Type         | Null | Key | Default | Extra |
# +-------+--------------+------+-----+---------+-------+
# | id    | int(11)      | NO   | PRI | NULL    |       |
# | name  | varchar(256) | YES  |     | NULL    |       |
# +-------+--------------+------+-----+---------+-------+
# 
# The table `t` will be synced to ES index `test` and type `t`.
#[[rule]]
#schema = "test"
#table = "t"
#index = "test"
#type = "t"

# Wildcard table rule, the wildcard table must be in source tables 
# All tables which match the wildcard format will be synced to ES index `test` and type `t`.
# In this example, all tables must have same schema with above table `t`;
#[[rule]]
#schema = "test"
#table = "t_[0-9]{4}"
#index = "test"
#type = "t"

# Simple field rule 
#
# desc tfield;
# +----------+--------------+------+-----+---------+-------+
# | Field    | Type         | Null | Key | Default | Extra |
# +----------+--------------+------+-----+---------+-------+
# | id       | int(11)      | NO   | PRI | NULL    |       |
# | tags     | varchar(256) | YES  |     | NULL    |       |
# | keywords | varchar(256) | YES  |     | NULL    |       |
# +----------+--------------+------+-----+---------+-------+
#
#[[rule]]
#schema = "test"
##table = "tfield"
#index = "test"
#type = "tfield"

#[rule.field]
# Map column `id` to ES field `es_id`
#id="es_id"
# Map column `tags` to ES field `es_tags` with array type 
#tags="es_tags,list"
# Map column `keywords` to ES with array type
#keywords=",list"

# Filter rule 
#
# desc tfilter;
# +-------+--------------+------+-----+---------+-------+
# | Field | Type         | Null | Key | Default | Extra |
# +-------+--------------+------+-----+---------+-------+
# | id    | int(11)      | NO   | PRI | NULL    |       |
# | c1    | int(11)      | YES  |     | 0       |       |
# | c2    | int(11)      | YES  |     | 0       |       |
# | name  | varchar(256) | YES  |     | NULL    |       |
# +-------+--------------+------+-----+---------+-------+
#
#[[rule]]
#schema = "test"
#table = "tfilter"
#index = "test"
#type = "tfilter"

# Only sync following columns
#filter = ["id", "name"]

# id rule
#
# desc tid_[0-9]{4};
# +----------+--------------+------+-----+---------+-------+
# | Field    | Type         | Null | Key | Default | Extra |
# +----------+--------------+------+-----+---------+-------+
# | id       | int(11)      | NO   | PRI | NULL    |       |
# | tag      | varchar(256) | YES  |     | NULL    |       |
# | desc     | varchar(256) | YES  |     | NULL    |       |
# +----------+--------------+------+-----+---------+-------+
#
#[[rule]]
#schema = "test"
#table = "tid_[0-9]{4}"
#index = "test"
#type = "t"
# The es doc's id will be `id`:`tag`
# It is useful for merge muliple table into one type while theses tables have same PK 
#id = ["id", "tag"]


  • 挂载文件夹/mysqlmaster/:/go/src/github.com/siddontang/go-mysql-elasticsearch/var为重启后上次同步信息备份
  • 完成
©著作权归作者所有,转载或内容合作请联系作者
  • 序言:七十年代末,一起剥皮案震惊了整个滨河市,随后出现的几起案子,更是在滨河造成了极大的恐慌,老刑警刘岩,带你破解...
    沈念sama阅读 203,937评论 6 478
  • 序言:滨河连续发生了三起死亡事件,死亡现场离奇诡异,居然都是意外死亡,警方通过查阅死者的电脑和手机,发现死者居然都...
    沈念sama阅读 85,503评论 2 381
  • 文/潘晓璐 我一进店门,熙熙楼的掌柜王于贵愁眉苦脸地迎上来,“玉大人,你说我怎么就摊上这事。” “怎么了?”我有些...
    开封第一讲书人阅读 150,712评论 0 337
  • 文/不坏的土叔 我叫张陵,是天一观的道长。 经常有香客问我,道长,这世上最难降的妖魔是什么? 我笑而不...
    开封第一讲书人阅读 54,668评论 1 276
  • 正文 为了忘掉前任,我火速办了婚礼,结果婚礼上,老公的妹妹穿的比我还像新娘。我一直安慰自己,他们只是感情好,可当我...
    茶点故事阅读 63,677评论 5 366
  • 文/花漫 我一把揭开白布。 她就那样静静地躺着,像睡着了一般。 火红的嫁衣衬着肌肤如雪。 梳的纹丝不乱的头发上,一...
    开封第一讲书人阅读 48,601评论 1 281
  • 那天,我揣着相机与录音,去河边找鬼。 笑死,一个胖子当着我的面吹牛,可吹牛的内容都是我干的。 我是一名探鬼主播,决...
    沈念sama阅读 37,975评论 3 396
  • 文/苍兰香墨 我猛地睁开眼,长吁一口气:“原来是场噩梦啊……” “哼!你这毒妇竟也来了?” 一声冷哼从身侧响起,我...
    开封第一讲书人阅读 36,637评论 0 258
  • 序言:老挝万荣一对情侣失踪,失踪者是张志新(化名)和其女友刘颖,没想到半个月后,有当地人在树林里发现了一具尸体,经...
    沈念sama阅读 40,881评论 1 298
  • 正文 独居荒郊野岭守林人离奇死亡,尸身上长有42处带血的脓包…… 初始之章·张勋 以下内容为张勋视角 年9月15日...
    茶点故事阅读 35,621评论 2 321
  • 正文 我和宋清朗相恋三年,在试婚纱的时候发现自己被绿了。 大学时的朋友给我发了我未婚夫和他白月光在一起吃饭的照片。...
    茶点故事阅读 37,710评论 1 329
  • 序言:一个原本活蹦乱跳的男人离奇死亡,死状恐怖,灵堂内的尸体忽然破棺而出,到底是诈尸还是另有隐情,我是刑警宁泽,带...
    沈念sama阅读 33,387评论 4 319
  • 正文 年R本政府宣布,位于F岛的核电站,受9级特大地震影响,放射性物质发生泄漏。R本人自食恶果不足惜,却给世界环境...
    茶点故事阅读 38,971评论 3 307
  • 文/蒙蒙 一、第九天 我趴在偏房一处隐蔽的房顶上张望。 院中可真热闹,春花似锦、人声如沸。这庄子的主人今日做“春日...
    开封第一讲书人阅读 29,947评论 0 19
  • 文/苍兰香墨 我抬头看了看天上的太阳。三九已至,却和暖如春,着一层夹袄步出监牢的瞬间,已是汗流浃背。 一阵脚步声响...
    开封第一讲书人阅读 31,189评论 1 260
  • 我被黑心中介骗来泰国打工, 没想到刚下飞机就差点儿被人妖公主榨干…… 1. 我叫王不留,地道东北人。 一个月前我还...
    沈念sama阅读 44,805评论 2 349
  • 正文 我出身青楼,却偏偏与公主长得像,于是被迫代替她去往敌国和亲。 传闻我的和亲对象是个残疾皇子,可洞房花烛夜当晚...
    茶点故事阅读 42,449评论 2 342

推荐阅读更多精彩内容