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# Binaries for programs and plugins
*.exe
*.exe~
*.dll
*.so
*.dylib
# Test binary, built with `go test -c`
*.test
# Output of the go coverage tool, specifically when used with LiteIDE
*.out
# Dependency directories (remove the comment below to include it)
# vendor/
.idea/
.vscode/
\ No newline at end of file
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GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
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Notwithstanding any other provision of this License, if you modify the
Program, your modified version must prominently offer all users
interacting with it remotely through a computer network (if your version
supports such interaction) an opportunity to receive the Corresponding
Source of your version by providing access to the Corresponding Source
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of the GNU General Public License that is incorporated pursuant to the
following paragraph.
Notwithstanding any other provision of this License, you have
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If the Program specifies that a proxy can decide which future
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THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
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ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
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IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
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GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
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17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
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END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
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state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If your software can interact with users remotely through a computer
network, you should also make sure that it provides a way for users to
get its source. For example, if your program is a web application, its
interface could display a "Source" link that leads users to an archive
of the code. There are many ways you could offer source, and different
solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
<https://www.gnu.org/licenses/>.
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浏览文件 @ f96c0195
## 样例数据导入
该工具可以根据用户提供的 `json` 或 `csv` 格式样例数据文件快速导入 `TDengine`，目前仅能在Linux上运行。
为了体验写入和查询性能，可以对样例数据进行横向、纵向扩展。横向扩展是指将一个表（监测点）的数据克隆到多张表，纵向扩展是指将样例数据中的一段时间范围内的数据在时间轴上复制。该工具还支持历史数据导入至当前时间后持续导入，这样可以测试插入和查询并行进行的场景，以模拟真实环境。
## 下载安装
### 下载可执行文件
由于该工具使用 go 语言开发，为了方便使用，项目中已经提供了编译好的可执行文件 `bin/timport`。通过 `git clone https://github.com/taosdata/TDengine.git` 命令或者直接下载 `ZIP` 文件解压进入样例导入程序目录 `cd importSampleData`，执行 `bin/taosimport`。
### go 源码编译
由于该工具使用 go 语言开发，编译之前需要先安装 go，具体请参考 [Getting Started][2]，而且需要安装 TDengine 的 Go Connector, 具体请参考[TDengine 连接器文档][3]。安装完成之后，执行以下命令即可编译成可执行文件 `bin/taosimport`。
```shell
go get https://github.com/taosdata/TDengine/importSampleData
cd $GOPATH/src/github.com/taosdata/TDengine/importSampleData
go build -o bin/taosimport app/main.go
```
> 注：由于目前 TDengine 的 go connector 只支持 linux 环境，所以该工具暂时只能在 linux 系统中运行。
> 如果 go get 失败可以下载之后复制 `github.com/taosdata/TDengine/importSampleData` 文件夹到 $GOPATH 的 src 目录下再执行 `go build -o bin/taosimport app/main.go`。
## 使用
### 快速体验
执行命令 `bin/taosimport` 会根据默认配置执行以下操作：
1. 创建数据库
自动创建名称为 `test_yyyyMMdd` 的数据库。
2. 创建超级表
根据配置文件 `config/cfg.toml` 中指定的 `sensor_info` 场景信息创建相应的超级表。
> 建表语句： create table s_sensor_info(ts timestamp, temperature int, humidity float) tags(location binary(20), color binary(16), devgroup int);
3. 自动建立子表并插入数据
根据配置文件 `config/cfg.toml` 中 `sensor_info` 场景指定的 `data/sensor_info.csv` 样例数据进行横向扩展 `100` 倍(可通过 hnum 参数指定)，即自动创建 `10*100=1000` 张子表(默认样例数据中有 10 张子表，每张表 100 条数据)，启动 `10` 个线程（可通过 thread 参数指定）对每张子表循环导入 `1000` 次(可通过 vnum 参数指定)。
进入 `taos shell`，可运行如下查询验证：
* 查询记录数
```shell
taos> use test_yyyyMMdd;
taos> select count(*) from s_sensor_info;
```
* 查询各个分组的记录数
```shell
taos> select count(*) from s_sensor_info group by devgroup;
```
* 按 1h 间隔查询各聚合指标
```shell
taos> select count(temperature), sum(temperature), avg(temperature) from s_sensor_info interval(1h);
```
* 查询指定位置最新上传指标
```shell
taos> select last(*) from s_sensor_info where location = 'beijing';
```
> 更多查询及函数使用请参考 [数据查询][4]
### 详细使用说明
执行命令 `bin/taosimport -h` 可以查看详细参数使用说明：
* -cfg string
导入配置文件路径，包含样例数据文件相关描述及对应 TDengine 配置信息。默认使用 `config/cfg.toml`。
* -cases string
需要导入的场景名称，该名称可从 -cfg 指定的配置文件中 `[usecase]` 查看，可同时导入多个场景，中间使用逗号分隔，如：`sensor_info,camera_detection`，默认为 `sensor_info`。
* -hnum int
需要将样例数据进行横向扩展的倍数，假设原有样例数据包含 1 张子表 `t_0` 数据，指定 hnum 为 2 时会根据原有表名创建 `t_0、t_1` 两张子表。默认为 100。
* -vnum int
需要将样例数据进行纵向扩展的次数，如果设置为 0 代表将历史数据导入至当前时间后持续按照指定间隔导入。默认为 1000，表示将样例数据在时间轴上纵向复制1000次。
* -delay int
当 vnum 设置为 0 时持续导入的时间间隔，默认 3000 ms。
* -auto int
是否自动生成样例数据中的主键时间戳，1 是，0 否， 默认 0。
* -start string
导入的记录开始时间，格式为 `"yyyy-MM-dd HH:mm:ss.SSS"`，不设置会使用样例数据中最小时间，设置后会忽略样例数据中的主键时间，会按照指定的 start 进行导入。如果 auto 为 1，则必须设置 start，默认为空。
* -interval int
导入的记录时间间隔，该设置只会在指定 `auto=1` 之后生效，否则会根据样例数据自动计算间隔时间。单位为毫秒，默认 1000。
* -thread int
执行导入数据的线程数目，默认为 10。
* -batch int
执行导入数据时的批量大小，默认为 100。批量是指一次写操作时，包含多少条记录。
* -host string
导入的 TDengine 服务器 IP，默认为 127.0.0.1。
* -port int
导入的 TDengine 服务器端口，默认为 6030。
* -user string
导入的 TDengine 用户名，默认为 root。
* -password string
导入的 TDengine 用户密码，默认为 taosdata。
* -dropdb int
导入数据之前是否删除数据库，1 是，0 否， 默认 0。
* -db string
导入的 TDengine 数据库名称，默认为 test_yyyyMMdd。
* -dbparam string
当指定的数据库不存在时，自动创建数据库时可选项配置参数，如 `days 10 cache 16000 ablocks 4`，默认为空。
### 常见使用示例
* `bin/taosimport -cfg config/cfg.toml -cases sensor_info,camera_detection -hnum 1 -vnum 10`
执行上述命令后会将 sensor_info、camera_detection 两个场景的数据各导入 10 次。
* `bin/taosimport -cfg config/cfg.toml -cases sensor_info -hnum 2 -vnum 0 -start "2019-12-12 00:00:00.000" -interval 5000`
执行上述命令后会将 sensor_info 场景的数据横向扩展2倍从指定时间 `2019-12-12 00:00:00.000` 开始且记录间隔时间为 5000 毫秒开始导入，导入至当前时间后会自动持续导入。
### config/cfg.toml 配置文件说明
``` toml
# 传感器场景
[sensor_info] # 场景名称
format = "csv" # 样例数据文件格式，可以是 json 或 csv，具体字段应至少包含 subTableName、tags、fields 指定的字段。
filePath = "data/sensor_info.csv" # 样例数据文件路径，程序会循环使用该文件数据
separator = "," # csv 样例文件中字段分隔符，默认逗号
stname = "sensor_info" # 超级表名称
subTableName = "devid" # 使用样例数据中指定字段当作子表名称一部分，子表名称格式为 t_subTableName_stname，扩展表名为 t_subTableName_stname_i。
timestamp = "ts" # 使用 fields 中哪个字段当作主键，类型必须为 timestamp
timestampType="millisecond" # 样例数据中主键时间字段是 millisecond 还是 dateTime 格式
#timestampTypeFormat = "2006-01-02 15:04:05.000" # 主键日期时间格式，timestampType 为 dateTime 时需要指定
tags = [
# 标签列表，name 为标签名称，type 为标签类型
{ name = "location", type = "binary(20)" },
{ name = "color", type = "binary(16)" },
{ name = "devgroup", type = "int" },
]
fields = [
# 字段列表，name 为字段名称，type 为字段类型
{ name = "ts", type = "timestamp" },
{ name = "temperature", type = "int" },
{ name = "humidity", type = "float" },
]
# 摄像头检测场景
[camera_detection] # 场景名称
format = "json" # 样例数据文件格式，可以是 json 或 csv，具体字段应至少包含 subTableName、tags、fields 指定的字段。
filePath = "data/camera_detection.json" # 样例数据文件路径，程序会循环使用该文件数据
#separator = "," # csv 样例文件中字段分隔符，默认逗号, 如果是 json 文件可以不用配置
stname = "camera_detection" # 超级表名称
subTableName = "sensor_id" # 使用样例数据中指定字段当作子表名称一部分，子表名称格式为 t_subTableName_stname，扩展表名为 t_subTableName_stname_i。
timestamp = "ts" # 使用 fields 中哪个字段当作主键，类型必须为 timestamp
timestampType="dateTime" # 样例数据中主键时间字段是 millisecond 还是 dateTime 格式
timestampTypeFormat = "2006-01-02 15:04:05.000" # 主键日期时间格式，timestampType 为 dateTime 时需要指定
tags = [
# 标签列表，name 为标签名称，type 为标签类型
{ name = "home_id", type = "binary(30)" },
{ name = "object_type", type = "int" },
{ name = "object_kind", type = "binary(20)" },
]
fields = [
# 字段列表，name 为字段名称，type 为字段类型
{ name = "ts", type = "timestamp" },
{ name = "states", type = "tinyint" },
{ name = "battery_voltage", type = "float" },
]
# other cases
```
### 样例数据格式说明
#### json
当配置文件 `config/cfg.toml` 中各场景的 format="json" 时，样例数据文件需要提供 tags 和 fields 字段列表中的字段值。样例数据格式如下：
```json
{"home_id": "603", "sensor_id": "s100", "ts": "2019-01-01 00:00:00.000", "object_type": 1, "object_kind": "night", "battery_voltage": 0.8, "states": 1}
{"home_id": "604", "sensor_id": "s200", "ts": "2019-01-01 00:00:00.000", "object_type": 2, "object_kind": "day", "battery_voltage": 0.6, "states": 0}
```
#### csv
当配置文件 `config/cfg.toml` 中各场景的 format="csv" 时，样例数据文件需要提供表头和对应的数据，其中字段分隔符由使用场景中 `separator` 指定，默认逗号。具体格式如下：
```csv
devid,location,color,devgroup,ts,temperature,humidity
0, beijing, white, 0, 1575129600000, 16, 19.405091
0, beijing, white, 0, 1575129601000, 22, 14.377142
```
[1]: https://github.com/taosdata/TDengine
[2]: https://golang.org/doc/install
[3]: https://www.taosdata.com/cn/documentation/connector/#Go-Connector
[4]: https://www.taosdata.com/cn/documentation/taos-sql/#%E6%95%B0%E6%8D%AE%E6%9F%A5%E8%AF%A2
\ No newline at end of file
importSampleData/app/main.go 0 → 100644
浏览文件 @ f96c0195
package main
import (
"bufio"
"bytes"
"database/sql"
"encoding/json"
"flag"
"fmt"
"github.com/taosdata/TDengine/importSampleData/import"
"hash/crc32"
"io"
"log"
"os"
"sort"
"strconv"
"strings"
"sync"
"sync/atomic"
"time"
_ "github.com/taosdata/TDengine/src/connector/go/src/taosSql"
)
const (
TIMESTAMP = "timestamp"
DATETIME = "datetime"
MILLISECOND = "millisecond"
DEFAULT_STARTTIME int64 = -1
DEFAULT_INTERVAL int64 = 1*1000
JSON_FORMAT = "json"
CSV_FORMAT = "csv"
SUPERTABLE_PREFIX = "s_"
SUBTABLE_PREFIX = "t_"
DRIVER_NAME = "taosSql"
STARTTIME_LAYOUT = "2006-01-02 15:04:05.000"
INSERT_PREFIX = "insert into "
)
var (
cfg string
cases string
hnum int
vnum int
thread int
batch int
auto int
starttimestr string
interval int64
host string
port int
user string
password string
dropdb int
db string
dbparam string
dataSourceName string
startTime int64
superTableConfigMap = make(map[string]*superTableConfig)
subTableMap = make(map[string]*dataRows)
scaleTableNames []string
scaleTableMap = make(map[string]*scaleTableInfo)
totalSuccessRows int64
delay int64 // default 10 milliseconds
)
type superTableConfig struct {
startTime int64
endTime int64
cycleTime int64
avgInterval int64
config dataimport.CaseConfig
}
type scaleTableInfo struct {
scaleTableName string
subTableName string
insertRows int64
}
type tableRows struct {
tableName string // tableName
value string // values(...)
}
type dataRows struct {
rows []map[string]interface{}
config dataimport.CaseConfig
}
func (rows dataRows) Len() int {
return len(rows.rows)
}
func (rows dataRows) Less(i, j int) bool {
itime := getPrimaryKey(rows.rows[i][rows.config.Timestamp])
jtime := getPrimaryKey(rows.rows[j][rows.config.Timestamp])
return itime < jtime
}
func (rows dataRows) Swap(i, j int) {
rows.rows[i], rows.rows[j] = rows.rows[j], rows.rows[i]
}
func getPrimaryKey(value interface{}) int64 {
val, _ := value.(int64)
//time, _ := strconv.ParseInt(str, 10, 64)
return val
}
func init() {
parseArg() //parse argument
if db == "" {
//db = "go"
db = fmt.Sprintf("test_%s",time.Now().Format("20060102"))
}
if auto == 1 && len(starttimestr) == 0 {
log.Fatalf("startTime must be set when auto is 1, the format is \"yyyy-MM-dd HH:mm:ss.SSS\" ")
}
if len(starttimestr) != 0 {
t, err := time.ParseInLocation(STARTTIME_LAYOUT, strings.TrimSpace(starttimestr), time.Local)
if err != nil {
log.Fatalf("param startTime %s error, %s\n", starttimestr, err)
}
startTime = t.UnixNano() / 1e6 // as millisecond
}else{
startTime = DEFAULT_STARTTIME
}
dataSourceName = fmt.Sprintf("%s:%s@/tcp(%s:%d)/", user, password, host, port)
printArg()
log.SetFlags(log.Ldate | log.Ltime | log.Lshortfile)
}
func main() {
importConfig := dataimport.LoadConfig(cfg)
for _, userCase := range strings.Split(cases, ",") {
caseConfig, ok := importConfig.UserCases[userCase]
if !ok {
log.Println("not exist case: ", userCase)
continue
}
checkUserCaseConfig(userCase, &caseConfig)
//read file as map array
fileRows := readFile(caseConfig)
log.Printf("case [%s] sample data file contains %d rows.\n", userCase, len(fileRows.rows))
if len(fileRows.rows) == 0 {
log.Printf("there is no valid line in file %s\n", caseConfig.FilePath)
continue
}
_, exists := superTableConfigMap[caseConfig.Stname]
if !exists {
superTableConfigMap[caseConfig.Stname] = &superTableConfig{config:caseConfig}
} else {
log.Fatalf("the stname of case %s already exist.\n", caseConfig.Stname)
}
var start, cycleTime, avgInterval int64 = getSuperTableTimeConfig(fileRows)
// set super table's startTime, cycleTime and avgInterval
superTableConfigMap[caseConfig.Stname].startTime = start
superTableConfigMap[caseConfig.Stname].avgInterval = avgInterval
superTableConfigMap[caseConfig.Stname].cycleTime = cycleTime
startStr := time.Unix(0, start*int64(time.Millisecond)).Format(STARTTIME_LAYOUT)
log.Printf("case [%s] startTime %s(%d), average dataInterval %d ms, cycleTime %d ms.\n", userCase, startStr, start, avgInterval, cycleTime)
}
superTableNum := len(superTableConfigMap)
if superTableNum == 0 {
log.Fatalln("no valid file, exited")
}
start := time.Now()
// create super table
createSuperTable(superTableConfigMap)
log.Printf("create %d superTable ,used %d ms.\n", superTableNum, time.Since(start)/1e6)
//create sub table
start = time.Now()
createSubTable(subTableMap)
log.Printf("create %d times of %d subtable ,all %d tables, used %d ms.\n", hnum, len(subTableMap), len(scaleTableMap), time.Since(start)/1e6)
subTableNum := len(scaleTableMap)
if subTableNum < thread {
thread = subTableNum
}
filePerThread := subTableNum / thread
leftFileNum := subTableNum % thread
var wg sync.WaitGroup
start = time.Now()
startIndex, endIndex := 0, filePerThread
for i := 0; i < thread; i++ {
// start thread
if i < leftFileNum {
endIndex++
}
wg.Add(1)
go insertData(i, startIndex, endIndex, &wg)
startIndex, endIndex = endIndex, endIndex+filePerThread
}
wg.Wait()
usedTime := time.Since(start)
log.Printf("finished insert %d rows, used %d ms, speed %d rows/s", totalSuccessRows, usedTime/1e6, totalSuccessRows * 1e9 / int64(usedTime))
if vnum == 0 {
// continue waiting for insert data
wait := make(chan string)
v := <- wait
log.Printf("program receive %s, exited.\n", v)
}
}
func getSuperTableTimeConfig(fileRows dataRows) (start, cycleTime, avgInterval int64){
if auto == 1 {
// use auto generate data time
start = startTime
avgInterval = interval
maxTableRows := normalizationDataWithSameInterval(fileRows, avgInterval)
cycleTime = maxTableRows * avgInterval + avgInterval
} else {
// use the sample data primary timestamp
sort.Sort(fileRows)// sort the file data by the primarykey
minTime := getPrimaryKey(fileRows.rows[0][fileRows.config.Timestamp])
maxTime := getPrimaryKey(fileRows.rows[len(fileRows.rows)-1][fileRows.config.Timestamp])
start = minTime // default startTime use the minTime
if DEFAULT_STARTTIME != startTime {
start = startTime
}
tableNum := normalizationData(fileRows, minTime)
if minTime == maxTime {
avgInterval = interval
cycleTime = tableNum * avgInterval + avgInterval
}else{
avgInterval = (maxTime - minTime) / int64(len(fileRows.rows)) * tableNum
cycleTime = maxTime - minTime + avgInterval
}
}
return
}
func createSubTable(subTableMaps map[string]*dataRows) {
connection := getConnection()
defer connection.Close()
connection.Exec("use " + db)
createTablePrefix := "create table if not exists "
for subTableName := range subTableMaps {
superTableName := getSuperTableName(subTableMaps[subTableName].config.Stname)
tagValues := subTableMaps[subTableName].rows[0] // the first rows values as tags
buffers := bytes.Buffer{}
// create table t using supertTable tags(...);
for i := 0; i < hnum; i++ {
tableName := getScaleSubTableName(subTableName, i)
scaleTableMap[tableName] = &scaleTableInfo{
subTableName: subTableName,
insertRows: 0,
}
scaleTableNames = append(scaleTableNames, tableName)
buffers.WriteString(createTablePrefix)
buffers.WriteString(tableName)
buffers.WriteString(" using ")
buffers.WriteString(superTableName)
buffers.WriteString(" tags(")
for _, tag := range subTableMaps[subTableName].config.Tags{
tagValue := fmt.Sprintf("%v", tagValues[strings.ToLower(tag.Name)])
buffers.WriteString("'" + tagValue + "'")
buffers.WriteString(",")
}
buffers.Truncate(buffers.Len()-1)
buffers.WriteString(")")
createTableSql := buffers.String()
buffers.Reset()
//log.Printf("create table: %s\n", createTableSql)
_, err := connection.Exec(createTableSql)
if err != nil {
log.Fatalf("create table error: %s\n", err)
}
}
}
}
func createSuperTable(superTableConfigMap map[string]*superTableConfig) {
connection := getConnection()
defer connection.Close()
if dropdb == 1 {
dropDbSql := "drop database if exists " + db
_, err := connection.Exec(dropDbSql) // drop database if exists
if err != nil {
log.Fatalf("drop database error: %s\n", err)
}
log.Printf("dropDb: %s\n", dropDbSql)
}
createDbSql := "create database if not exists " + db + " " + dbparam
_, err := connection.Exec(createDbSql) // create database if not exists
if err != nil {
log.Fatalf("create database error: %s\n", err)
}
log.Printf("createDb: %s\n", createDbSql)
connection.Exec("use " + db)
prefix := "create table if not exists "
var buffer bytes.Buffer
//CREATE TABLE <stable_name> (<field_name> TIMESTAMP, field_name1 field_type,…) TAGS(tag_name tag_type, …)
for key := range superTableConfigMap {
buffer.WriteString(prefix)
buffer.WriteString(getSuperTableName(key))
buffer.WriteString("(")
superTableConf := superTableConfigMap[key]
buffer.WriteString(superTableConf.config.Timestamp)
buffer.WriteString(" timestamp, ")
for _, field := range superTableConf.config.Fields {
buffer.WriteString(field.Name + " " + field.Type + ",")
}
buffer.Truncate(buffer.Len()-1)
buffer.WriteString(") tags( ")
for _, tag := range superTableConf.config.Tags {
buffer.WriteString(tag.Name + " " + tag.Type + ",")
}
buffer.Truncate(buffer.Len()-1)
buffer.WriteString(")")
createSql := buffer.String()
buffer.Reset()
//log.Printf("supertable: %s\n", createSql)
_, err = connection.Exec(createSql)
if err != nil {
log.Fatalf("create supertable error: %s\n", err)
}
}
}
func getScaleSubTableName(subTableName string, hnum int) string {
if hnum == 0 {
return subTableName
}
return fmt.Sprintf( "%s_%d", subTableName, hnum)
}
func getSuperTableName(stname string) string {
return SUPERTABLE_PREFIX + stname
}
/**
* normalizationData , and return the num of subTables
*/
func normalizationData(fileRows dataRows, minTime int64) int64 {
var tableNum int64 = 0
for _, row := range fileRows.rows {
// get subTableName
tableValue := getSubTableNameValue(row[fileRows.config.SubTableName])
if len(tableValue) == 0 {
continue
}
row[fileRows.config.Timestamp] = getPrimaryKey(row[fileRows.config.Timestamp]) - minTime
subTableName := getSubTableName(tableValue, fileRows.config.Stname)
value, ok := subTableMap[subTableName]
if !ok {
subTableMap[subTableName] = &dataRows{
rows: []map[string]interface{}{row},
config: fileRows.config,
}
tableNum++
}else{
value.rows = append(value.rows, row)
}
}
return tableNum
}
// return the maximum table rows
func normalizationDataWithSameInterval(fileRows dataRows, avgInterval int64) int64{
// subTableMap
currSubTableMap := make(map[string]*dataRows)
for _, row := range fileRows.rows {
// get subTableName
tableValue := getSubTableNameValue(row[fileRows.config.SubTableName])
if len(tableValue) == 0 {
continue
}
subTableName := getSubTableName(tableValue, fileRows.config.Stname)
value, ok := currSubTableMap[subTableName]
if !ok {
row[fileRows.config.Timestamp] = 0
currSubTableMap[subTableName] = &dataRows{
rows: []map[string]interface{}{row},
config: fileRows.config,
}
}else{
row[fileRows.config.Timestamp] = int64(len(value.rows)) * avgInterval
value.rows = append(value.rows, row)
}
}
var maxRows, tableRows int = 0, 0
for tableName := range currSubTableMap{
tableRows = len(currSubTableMap[tableName].rows)
subTableMap[tableName] = currSubTableMap[tableName] // add to global subTableMap
if tableRows > maxRows {
maxRows = tableRows
}
}
return int64(maxRows)
}
func getSubTableName(subTableValue string, superTableName string) string {
return SUBTABLE_PREFIX + subTableValue + "_" + superTableName
}
func insertData(threadIndex, start, end int, wg *sync.WaitGroup) {
connection := getConnection()
defer connection.Close()
defer wg.Done()
connection.Exec("use " + db) // use db
num := 0
for {
log.Printf("thread-%d start insert into [%d, %d) subtables.\n", threadIndex, start, end)
threadStartTime := time.Now()
var successRows int64
var rows []tableRows
subTables := scaleTableNames[start:end]
for _, tableName := range subTables {
subTableInfo := subTableMap[scaleTableMap[tableName].subTableName]
subTableRows := int64(len(subTableInfo.rows))
superTableConf := superTableConfigMap[subTableInfo.config.Stname]
tableStartTime := superTableConf.startTime
var tableEndTime int64
if vnum == 0 {
// need continue generate data
tableEndTime = time.Now().UnixNano()/1e6
}else {
tableEndTime = tableStartTime + superTableConf.cycleTime * int64(vnum) - superTableConf.avgInterval
}
insertRows := scaleTableMap[tableName].insertRows
for {
loopNum := insertRows / subTableRows
rowIndex := insertRows % subTableRows
currentRow := subTableInfo.rows[rowIndex]
currentTime := getPrimaryKey(currentRow[subTableInfo.config.Timestamp]) + loopNum * superTableConf.cycleTime + tableStartTime
if currentTime <= tableEndTime {
// append
row := buildRow(tableName, currentTime, subTableInfo, currentRow)
rows = append(rows, row)
insertRows++
if len(rows) == batch {
// executebatch
insertSql := buildSql(rows)
affectedRows := executeBatchInsert(insertSql, connection)
successRows = atomic.AddInt64(&successRows, affectedRows)
rows = []tableRows{}
}
}else {
// finished insert current table
break
}
}
scaleTableMap[tableName].insertRows = insertRows
}
left := len(rows)
if left > 0 {
// executebatch
insertSql := buildSql(rows)
affectedRows := executeBatchInsert(insertSql, connection)
successRows = atomic.AddInt64(&successRows, affectedRows)
}
atomic.AddInt64(&totalSuccessRows, successRows)
log.Printf("thread-%d finished insert %d rows, used %d ms.", threadIndex, successRows, time.Since(threadStartTime)/1e6)
if vnum != 0 {
// thread finished insert data
break
}
if(num == 0){
wg.Done() // finished insert history data
}
num++
// need continue insert data
// log.Printf("thread-%d start to sleep %d ms.", threadIndex, delay)
time.Sleep(time.Duration(delay) * time.Millisecond)
}
}
func buildSql(rows []tableRows) string{
var lastTableName string
buffers := bytes.Buffer{}
for i, row := range rows {
if i == 0 {
lastTableName = row.tableName
buffers.WriteString(INSERT_PREFIX)
buffers.WriteString(row.tableName)
buffers.WriteString(" values")
buffers.WriteString(row.value)
continue
}
if lastTableName == row.tableName {
buffers.WriteString(row.value)
}else {
buffers.WriteString(" ")
buffers.WriteString(row.tableName)
buffers.WriteString(" values")
buffers.WriteString(row.value)
lastTableName = row.tableName
}
}
inserSql := buffers.String()
return inserSql
}
func buildRow(tableName string, currentTime int64, subTableInfo *dataRows, currentRow map[string]interface{}) tableRows{
tableRows := tableRows{tableName: tableName}
buffers := bytes.Buffer{}
buffers.WriteString("(")
buffers.WriteString(fmt.Sprintf("%v", currentTime))
buffers.WriteString(",")
for _,field := range subTableInfo.config.Fields {
buffers.WriteString(getFieldValue(currentRow[strings.ToLower(field.Name)]))
buffers.WriteString(",")
}
buffers.Truncate(buffers.Len()-1)
buffers.WriteString(")")
insertSql := buffers.String()
tableRows.value = insertSql
return tableRows
}
func executeBatchInsert(insertSql string, connection *sql.DB) int64 {
result, error := connection.Exec(insertSql)
if error != nil {
log.Printf("execute insertSql %s error, %s\n", insertSql, error)
return 0
}
affected, _ := result.RowsAffected()
if affected < 0 {
affected = 0
}
return affected
}
func getFieldValue(fieldValue interface{}) string {
return fmt.Sprintf("'%v'", fieldValue)
}
func getConnection() *sql.DB{
db, err := sql.Open(DRIVER_NAME, dataSourceName)
if err != nil {
panic(err)
}
return db
}
func getSubTableNameValue(suffix interface{}) string {
return fmt.Sprintf("%v", suffix)
}
func hash(s string) int {
v := int(crc32.ChecksumIEEE([]byte(s)))
if v < 0 {
return -v
}
return v
}
func readFile(config dataimport.CaseConfig) dataRows {
fileFormat := strings.ToLower(config.Format)
if fileFormat == JSON_FORMAT {
return readJSONFile(config)
} else if fileFormat == CSV_FORMAT {
return readCSVFile(config)
}
log.Printf("the file %s is not supported yet\n", config.FilePath)
return dataRows{}
}
func readCSVFile(config dataimport.CaseConfig) dataRows {
var rows dataRows
f, err := os.Open(config.FilePath)
if err != nil {
log.Printf("Error: %s, %s\n", config.FilePath, err)
return rows
}
defer f.Close()
r := bufio.NewReader(f)
//read the first line as title
lineBytes, _, err := r.ReadLine()
if err == io.EOF {
log.Printf("the file %s is empty\n", config.FilePath)
return rows
}
line := strings.ToLower(string(lineBytes))
titles := strings.Split(line, config.Separator)
if len(titles) < 3 {
// need suffix、 primarykey and at least one other field
log.Printf("the first line of file %s should be title row, and at least 3 field.\n", config.FilePath)
return rows
}
rows.config = config
var lineNum = 0
for {
// read data row
lineBytes, _, err = r.ReadLine()
lineNum++
if err == io.EOF {
break
}
// fmt.Println(line)
rowData := strings.Split(string(lineBytes), config.Separator)
dataMap := make(map[string]interface{})
for i, title := range titles {
title = strings.TrimSpace(title)
if i < len(rowData) {
dataMap[title] = strings.TrimSpace(rowData[i])
} else {
dataMap[title] = ""
}
}
// if the suffix valid
if !existMapKeyAndNotEmpty(config.Timestamp, dataMap) {
log.Printf("the Timestamp[%s] of line %d is empty, will filtered.\n", config.Timestamp, lineNum)
continue
}
// if the primary key valid
primaryKeyValue := getPrimaryKeyMillisec(config.Timestamp, config.TimestampType, config.TimestampTypeFormat, dataMap)
if primaryKeyValue == -1 {
log.Printf("the Timestamp[%s] of line %d is not valid, will filtered.\n", config.Timestamp, lineNum)
continue
}
dataMap[config.Timestamp] = primaryKeyValue
rows.rows = append(rows.rows, dataMap)
}
return rows
}
func readJSONFile(config dataimport.CaseConfig) dataRows {
var rows dataRows
f, err := os.Open(config.FilePath)
if err != nil {
log.Printf("Error: %s, %s\n", config.FilePath, err)
return rows
}
defer f.Close()
r := bufio.NewReader(f)
//log.Printf("file size %d\n", r.Size())
rows.config = config
var lineNum = 0
for {
lineBytes, _, err := r.ReadLine()
lineNum++
if err == io.EOF {
break
}
line := make(map[string]interface{})
err = json.Unmarshal(lineBytes, &line)
if err != nil {
log.Printf("line [%d] of file %s parse error, reason: %s\n", lineNum, config.FilePath, err)
continue
}
// transfer the key to lowercase
lowerMapKey(line)
if !existMapKeyAndNotEmpty(config.SubTableName, line) {
log.Printf("the SubTableName[%s] of line %d is empty, will filtered.\n", config.SubTableName, lineNum)
continue
}
primaryKeyValue := getPrimaryKeyMillisec(config.Timestamp, config.TimestampType, config.TimestampTypeFormat, line)
if primaryKeyValue == -1 {
log.Printf("the Timestamp[%s] of line %d is not valid, will filtered.\n", config.Timestamp, lineNum)
continue
}
line[config.Timestamp] = primaryKeyValue
rows.rows = append(rows.rows, line)
}
return rows
}
/**
* get primary key as millisecond , otherwise return -1
*/
func getPrimaryKeyMillisec(key string, valueType string, valueFormat string, line map[string]interface{}) int64 {
if !existMapKeyAndNotEmpty(key, line) {
return -1
}
if DATETIME == valueType {
// transfer the datetime to milliseconds
return parseMillisecond(line[key], valueFormat)
}
value, err := strconv.ParseInt(fmt.Sprintf("%v", line[key]), 10, 64)
// as millisecond num
if err != nil {
return -1
}
return value
}
// parseMillisecond parse the dateStr to millisecond, return -1 if failed
func parseMillisecond(str interface{}, layout string) int64 {
value, ok := str.(string)
if !ok {
return -1
}
t, err := time.ParseInLocation(layout, strings.TrimSpace(value), time.Local)
if err != nil {
log.Println(err)
return -1
}
return t.UnixNano()/1e6
}
// lowerMapKey transfer all the map key to lowercase
func lowerMapKey(maps map[string]interface{}) {
for key := range maps {
value := maps[key]
delete(maps, key)
maps[strings.ToLower(key)] = value
}
}
func existMapKeyAndNotEmpty(key string, maps map[string]interface{}) bool {
value, ok := maps[key]
if !ok {
return false
}
str, err := value.(string)
if err && len(str) == 0 {
return false
}
return true
}
func checkUserCaseConfig(caseName string, caseConfig *dataimport.CaseConfig) {
if len(caseConfig.Stname) == 0 {
log.Fatalf("the stname of case %s can't be empty\n", caseName)
}
caseConfig.Stname = strings.ToLower(caseConfig.Stname)
if len(caseConfig.Tags) == 0 {
log.Fatalf("the tags of case %s can't be empty\n", caseName)
}
if len(caseConfig.Fields) == 0 {
log.Fatalf("the fields of case %s can't be empty\n", caseName)
}
if len(caseConfig.SubTableName) == 0 {
log.Fatalf("the suffix of case %s can't be empty\n", caseName)
}
caseConfig.SubTableName = strings.ToLower(caseConfig.SubTableName)
caseConfig.Timestamp = strings.ToLower(caseConfig.Timestamp)
var timestampExist = false
for i, field := range caseConfig.Fields {
if strings.EqualFold(field.Name, caseConfig.Timestamp) {
if strings.ToLower(field.Type) != TIMESTAMP {
log.Fatalf("case %s's primaryKey %s field type is %s, it must be timestamp\n", caseName, caseConfig.Timestamp, field.Type)
}
timestampExist = true
if i < len(caseConfig.Fields)-1 {
// delete middle item, a = a[:i+copy(a[i:], a[i+1:])]
caseConfig.Fields = caseConfig.Fields[:i+copy(caseConfig.Fields[i:], caseConfig.Fields[i+1:])]
}else {
// delete the last item
caseConfig.Fields = caseConfig.Fields[:len(caseConfig.Fields)-1]
}
break
}
}
if !timestampExist {
log.Fatalf("case %s primaryKey %s is not exist in fields\n", caseName, caseConfig.Timestamp)
}
caseConfig.TimestampType = strings.ToLower(caseConfig.TimestampType)
if caseConfig.TimestampType != MILLISECOND && caseConfig.TimestampType != DATETIME {
log.Fatalf("case %s's timestampType %s error, only can be timestamp or datetime\n", caseName, caseConfig.TimestampType)
}
if caseConfig.TimestampType == DATETIME && len(caseConfig.TimestampTypeFormat) == 0 {
log.Fatalf("case %s's timestampTypeFormat %s can't be empty when timestampType is datetime\n", caseName, caseConfig.TimestampTypeFormat)
}
}
func parseArg() {
flag.StringVar(&cfg, "cfg", "config/cfg.toml", "configuration file which describes usecase and data format.")
flag.StringVar(&cases, "cases", "sensor_info", "usecase for dataset to be imported. Multiple choices can be separated by comma, for example, -cases sensor_info,camera_detection.")
flag.IntVar(&hnum, "hnum", 100, "magnification factor of the sample tables. For example, if hnum is 100 and in the sample data there are 10 tables, then 10x100=1000 tables will be created in the database.")
flag.IntVar(&vnum, "vnum", 1000, "copies of the sample records in each table. If set to 0，this program will never stop simulating and importing data even if the timestamp has passed current time.")
flag.Int64Var(&delay, "delay", 3 * 1000, "the delay millisecond to continue generate data when vnum set 0.")
flag.IntVar(&thread, "thread", 10, "number of threads to import data.")
flag.IntVar(&batch, "batch", 100, "rows of records in one import batch.")
flag.IntVar(&auto, "auto", 0, "whether to use the starttime and interval specified by users when simulating the data. 0 is disabled and 1 is enabled.")
flag.StringVar(&starttimestr, "start", "", "the starting timestamp of simulated data, in the format of yyyy-MM-dd HH:mm:ss.SSS. If not specified, the ealiest timestamp in the sample data will be set as the starttime.")
flag.Int64Var(&interval, "interval", DEFAULT_INTERVAL, "time inteval between two consecutive records, in the unit of millisecond. Only valid when auto is 1.")
flag.StringVar(&host, "host", "127.0.0.1", "tdengine server ip.")
flag.IntVar(&port, "port", 6030, "tdengine server port.")
flag.StringVar(&user, "user", "root", "user name to login into the database.")
flag.StringVar(&password, "password", "taosdata", "the import tdengine user password")
flag.IntVar(&dropdb, "dropdb", 0, "whether to drop the existing datbase. 1 is yes and 0 otherwise.")
flag.StringVar(&db, "db", "", "name of the database to store data.")
flag.StringVar(&dbparam, "dbparam", "", "database configurations when it is created.")
flag.Parse()
}
func printArg() {
fmt.Println("used param: ")
fmt.Println("-cfg: ", cfg)
fmt.Println("-cases:", cases)
fmt.Println("-hnum:", hnum)
fmt.Println("-vnum:", vnum)
fmt.Println("-thread:", thread)
fmt.Println("-batch:", batch)
fmt.Println("-auto:", auto)
fmt.Println("-start:", starttimestr)
fmt.Println("-interval:", interval)
fmt.Println("-delay:", delay)
fmt.Println("-host:", host)
fmt.Println("-port", port)
fmt.Println("-user", user)
fmt.Println("-password", password)
fmt.Println("-dropdb", dropdb)
fmt.Println("-db", db)
fmt.Println("-dbparam", dbparam)
}
importSampleData/config/cfg.toml 0 → 100644
浏览文件 @ f96c0195
# 传感器场景
[sensor_info] # 场景名称
format = "csv" # 样例数据文件格式，可以是 json 或 csv，具体字段应至少包含 subTableName、tags、fields 指定的字段。
filePath = "data/sensor_info.csv" # 样例数据文件路径，程序会循环使用该文件数据
separator = "," # csv 样例文件中字段分隔符，默认逗号
stname = "sensor_info" # 超级表名称
subTableName = "devid" # 使用样例数据中指定字段当作子表名称一部分，子表名称格式为 t_subTableName_stname，扩展表名为 t_subTableName_stname_i。
timestamp = "ts" # 使用 fields 中哪个字段当作主键，类型必须为 timestamp
timestampType="millisecond" # 样例数据中主键时间字段是 millisecond 还是 dateTime 格式
#timestampTypeFormat = "2006-01-02 15:04:05.000" # 主键日期时间格式，timestampType 为 dateTime 时需要指定
tags = [
# 标签列表，name 为标签名称，type 为标签类型
{ name = "location", type = "binary(20)" },
{ name = "color", type = "binary(16)" },
{ name = "devgroup", type = "int" },
]
fields = [
# 字段列表，name 为字段名称，type 为字段类型
{ name = "ts", type = "timestamp" },
{ name = "temperature", type = "int" },
{ name = "humidity", type = "float" },
]
# 摄像头检测场景
[camera_detection] # 场景名称
format = "json" # 样例数据文件格式，可以是 json 或 csv，具体字段应至少包含 subTableName、tags、fields 指定的字段。
filePath = "data/camera_detection.json" # 样例数据文件路径，程序会循环使用该文件数据
#separator = "," # csv 样例文件中字段分隔符，默认逗号, 如果是 json 文件可以不用配置
stname = "camera_detection" # 超级表名称
subTableName = "sensor_id" # 使用样例数据中指定字段当作子表名称一部分，子表名称格式为 t_subTableName_stname，扩展表名为 t_subTableName_stname_i。
timestamp = "ts" # 使用 fields 中哪个字段当作主键，类型必须为 timestamp
timestampType="dateTime" # 样例数据中主键时间字段是 millisecond 还是 dateTime 格式
timestampTypeFormat = "2006-01-02 15:04:05.000" # 主键日期时间格式，timestampType 为 dateTime 时需要指定
tags = [
# 标签列表，name 为标签名称，type 为标签类型
{ name = "home_id", type = "binary(30)" },
{ name = "object_type", type = "int" },
{ name = "object_kind", type = "binary(20)" },
]
fields = [
# 字段列表，name 为字段名称，type 为字段类型
{ name = "ts", type = "timestamp" },
{ name = "states", type = "tinyint" },
{ name = "battery_voltage", type = "float" },
]
# other case
\ No newline at end of file
importSampleData/data/camera_detection.json 0 → 100644
浏览文件 @ f96c0195
{"battery_voltage":0.80233014,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:00.000"}
{"battery_voltage":0.83228004,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:01.000"}
{"battery_voltage":0.7123188,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:02.000"}
{"battery_voltage":0.5328185,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:03.000"}
{"battery_voltage":0.54848474,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:04.000"}
{"battery_voltage":0.7576063,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:05.000"}
{"battery_voltage":0.60713196,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:06.000"}
{"battery_voltage":0.65902907,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:07.000"}
{"battery_voltage":0.64151704,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:08.000"}
{"battery_voltage":0.8395423,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:09.000"}
{"battery_voltage":0.60159343,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:10.000"}
{"battery_voltage":0.7853366,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:11.000"}
{"battery_voltage":0.6465571,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:12.000"}
{"battery_voltage":0.8762865,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:13.000"}
{"battery_voltage":0.9326675,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:14.000"}
{"battery_voltage":0.76191014,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:15.000"}
{"battery_voltage":0.57916415,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:16.000"}
{"battery_voltage":0.98762083,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:17.000"}
{"battery_voltage":0.7974043,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:18.000"}
{"battery_voltage":0.8460123,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:19.000"}
{"battery_voltage":0.5866331,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:20.000"}
{"battery_voltage":0.7720778,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:21.000"}
{"battery_voltage":0.7115761,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:22.000"}
{"battery_voltage":0.62677026,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:23.000"}
{"battery_voltage":0.8943025,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:24.000"}
{"battery_voltage":0.94027156,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:25.000"}
{"battery_voltage":0.94718087,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:26.000"}
{"battery_voltage":0.9884584,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:27.000"}
{"battery_voltage":0.6111447,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:28.000"}
{"battery_voltage":0.6207575,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:29.000"}
{"battery_voltage":0.9664232,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:30.000"}
{"battery_voltage":0.9005275,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:31.000"}
{"battery_voltage":0.59146243,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:32.000"}
{"battery_voltage":0.948496,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:33.000"}
{"battery_voltage":0.98946464,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:34.000"}
{"battery_voltage":0.5454186,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:35.000"}
{"battery_voltage":0.9634934,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:36.000"}
{"battery_voltage":0.673977,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:37.000"}
{"battery_voltage":0.8554536,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:38.000"}
{"battery_voltage":0.8247447,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":0,"ts":"2019-12-01 00:00:39.000"}
{"battery_voltage":0.87791175,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:40.000"}
{"battery_voltage":0.56532556,"home_id":"603","object_kind":"night","object_type":1,"sensor_id":"s100","states":1,"ts":"2019-12-01 00:00:41.000"}
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importSampleData/data/sensor_info.csv 0 → 100644
浏览文件 @ f96c0195
devid,location,color,devgroup,ts,temperature,humidity
0, beijing, white, 0, 1575129600000, 16, 19.405091
0, beijing, white, 0, 1575129601000, 22, 14.377142
0, beijing, white, 0, 1575129602000, 16, 16.868231
0, beijing, white, 0, 1575129603000, 20, 11.565193
0, beijing, white, 0, 1575129604000, 31, 13.009119
0, beijing, white, 0, 1575129605000, 29, 18.136400
0, beijing, white, 0, 1575129606000, 17, 13.806572
0, beijing, white, 0, 1575129607000, 23, 14.688898
0, beijing, white, 0, 1575129608000, 26, 12.931019
0, beijing, white, 0, 1575129609000, 32, 12.185531
0, beijing, white, 0, 1575129610000, 30, 13.608714
0, beijing, white, 0, 1575129611000, 23, 18.624914
0, beijing, white, 0, 1575129612000, 22, 12.970826
0, beijing, white, 0, 1575129613000, 22, 12.065827
0, beijing, white, 0, 1575129614000, 25, 16.967192
0, beijing, white, 0, 1575129615000, 16, 10.283031
0, beijing, white, 0, 1575129616000, 22, 16.072534
0, beijing, white, 0, 1575129617000, 24, 10.794536
0, beijing, white, 0, 1575129618000, 32, 10.591207
0, beijing, white, 0, 1575129619000, 20, 13.015227
0, beijing, white, 0, 1575129620000, 28, 15.410999
0, beijing, white, 0, 1575129621000, 29, 12.785076
0, beijing, white, 0, 1575129622000, 28, 15.305857
0, beijing, white, 0, 1575129623000, 33, 12.820810
0, beijing, white, 0, 1575129624000, 34, 13.618055
0, beijing, white, 0, 1575129625000, 32, 12.971123
0, beijing, white, 0, 1575129626000, 24, 10.974546
0, beijing, white, 0, 1575129627000, 15, 10.742910
0, beijing, white, 0, 1575129628000, 23, 16.810783
0, beijing, white, 0, 1575129629000, 18, 13.115224
0, beijing, white, 0, 1575129630000, 26, 17.418490
0, beijing, white, 0, 1575129631000, 20, 17.302315
0, beijing, white, 0, 1575129632000, 21, 14.283571
0, beijing, white, 0, 1575129633000, 16, 16.826535
0, beijing, white, 0, 1575129634000, 18, 19.222123
0, beijing, white, 0, 1575129635000, 18, 14.931420
0, beijing, white, 0, 1575129636000, 17, 19.549454
0, beijing, white, 0, 1575129637000, 22, 16.908388
0, beijing, white, 0, 1575129638000, 32, 15.637796
0, beijing, white, 0, 1575129639000, 31, 15.517650
0, beijing, white, 0, 1575129640000, 18, 14.038033
0, beijing, white, 0, 1575129641000, 32, 19.859647
0, beijing, white, 0, 1575129642000, 16, 13.220840
0, beijing, white, 0, 1575129643000, 28, 16.445398
0, beijing, white, 0, 1575129644000, 26, 16.695753
0, beijing, white, 0, 1575129645000, 33, 13.696928
0, beijing, white, 0, 1575129646000, 21, 15.352819
0, beijing, white, 0, 1575129647000, 15, 12.388407
0, beijing, white, 0, 1575129648000, 27, 11.267529
0, beijing, white, 0, 1575129649000, 20, 14.103228
0, beijing, white, 0, 1575129650000, 20, 16.250950
0, beijing, white, 0, 1575129651000, 30, 16.236088
0, beijing, white, 0, 1575129652000, 22, 18.305339
0, beijing, white, 0, 1575129653000, 25, 17.360686
0, beijing, white, 0, 1575129654000, 25, 14.978681
0, beijing, white, 0, 1575129655000, 33, 14.096183
0, beijing, white, 0, 1575129656000, 26, 10.019039
0, beijing, white, 0, 1575129657000, 19, 19.158213
0, beijing, white, 0, 1575129658000, 22, 15.593924
0, beijing, white, 0, 1575129659000, 26, 18.780118
0, beijing, white, 0, 1575129660000, 21, 16.001656
0, beijing, white, 0, 1575129661000, 16, 18.458328
0, beijing, white, 0, 1575129662000, 21, 16.417843
0, beijing, white, 0, 1575129663000, 28, 11.736558
0, beijing, white, 0, 1575129664000, 34, 18.143946
0, beijing, white, 0, 1575129665000, 27, 10.303225
0, beijing, white, 0, 1575129666000, 20, 19.756748
0, beijing, white, 0, 1575129667000, 22, 12.940063
0, beijing, white, 0, 1575129668000, 23, 11.509640
0, beijing, white, 0, 1575129669000, 19, 18.319309
0, beijing, white, 0, 1575129670000, 19, 16.278346
0, beijing, white, 0, 1575129671000, 27, 10.898361
0, beijing, white, 0, 1575129672000, 31, 13.922162
0, beijing, white, 0, 1575129673000, 15, 19.296116
0, beijing, white, 0, 1575129674000, 26, 15.885763
0, beijing, white, 0, 1575129675000, 15, 15.525804
0, beijing, white, 0, 1575129676000, 19, 19.579539
0, beijing, white, 0, 1575129677000, 20, 11.073811
0, beijing, white, 0, 1575129678000, 16, 13.932510
0, beijing, white, 0, 1575129679000, 17, 11.900328
0, beijing, white, 0, 1575129680000, 22, 16.540414
0, beijing, white, 0, 1575129681000, 33, 15.203803
0, beijing, white, 0, 1575129682000, 17, 11.518434
0, beijing, white, 0, 1575129683000, 17, 13.152081
0, beijing, white, 0, 1575129684000, 18, 11.378041
0, beijing, white, 0, 1575129685000, 21, 15.390745
0, beijing, white, 0, 1575129686000, 30, 15.127818
0, beijing, white, 0, 1575129687000, 19, 16.530402
0, beijing, white, 0, 1575129688000, 32, 16.542701
0, beijing, white, 0, 1575129689000, 26, 16.366442
0, beijing, white, 0, 1575129690000, 25, 10.306822
0, beijing, white, 0, 1575129691000, 15, 13.691117
0, beijing, white, 0, 1575129692000, 15, 13.476817
0, beijing, white, 0, 1575129693000, 25, 12.529998
0, beijing, white, 0, 1575129694000, 22, 15.550021
0, beijing, white, 0, 1575129695000, 20, 15.064971
0, beijing, white, 0, 1575129696000, 24, 13.313683
0, beijing, white, 0, 1575129697000, 23, 17.002879
0, beijing, white, 0, 1575129698000, 30, 19.991595
0, beijing, white, 0, 1575129699000, 15, 11.116746
1, shanghai, black, 1, 1575129600000, 24, 10.921176
1, shanghai, black, 1, 1575129601000, 26, 17.146958
1, shanghai, black, 1, 1575129602000, 21, 18.486329
1, shanghai, black, 1, 1575129603000, 34, 12.125609
1, shanghai, black, 1, 1575129604000, 22, 19.451948
1, shanghai, black, 1, 1575129605000, 23, 16.458334
1, shanghai, black, 1, 1575129606000, 18, 14.484644
1, shanghai, black, 1, 1575129607000, 33, 10.824797
1, shanghai, black, 1, 1575129608000, 34, 14.001883
1, shanghai, black, 1, 1575129609000, 32, 19.498832
1, shanghai, black, 1, 1575129610000, 30, 14.993855
1, shanghai, black, 1, 1575129611000, 28, 10.198087
1, shanghai, black, 1, 1575129612000, 32, 14.286884
1, shanghai, black, 1, 1575129613000, 25, 18.874475
1, shanghai, black, 1, 1575129614000, 21, 17.650082
1, shanghai, black, 1, 1575129615000, 15, 17.275773
1, shanghai, black, 1, 1575129616000, 17, 15.130875
1, shanghai, black, 1, 1575129617000, 16, 17.242291
1, shanghai, black, 1, 1575129618000, 15, 19.777635
1, shanghai, black, 1, 1575129619000, 29, 18.321979
1, shanghai, black, 1, 1575129620000, 15, 19.133991
1, shanghai, black, 1, 1575129621000, 16, 18.351038
1, shanghai, black, 1, 1575129622000, 31, 17.517406
1, shanghai, black, 1, 1575129623000, 34, 10.969342
1, shanghai, black, 1, 1575129624000, 28, 15.838347
1, shanghai, black, 1, 1575129625000, 19, 19.982738
1, shanghai, black, 1, 1575129626000, 24, 19.854656
1, shanghai, black, 1, 1575129627000, 34, 13.320561
1, shanghai, black, 1, 1575129628000, 15, 19.560206
1, shanghai, black, 1, 1575129629000, 15, 11.843907
1, shanghai, black, 1, 1575129630000, 19, 18.332418
1, shanghai, black, 1, 1575129631000, 30, 18.058718
1, shanghai, black, 1, 1575129632000, 16, 17.185304
1, shanghai, black, 1, 1575129633000, 29, 18.958033
1, shanghai, black, 1, 1575129634000, 25, 10.187132
1, shanghai, black, 1, 1575129635000, 33, 14.235532
1, shanghai, black, 1, 1575129636000, 19, 14.326982
1, shanghai, black, 1, 1575129637000, 29, 18.557044
1, shanghai, black, 1, 1575129638000, 19, 16.590305
1, shanghai, black, 1, 1575129639000, 21, 15.034868
1, shanghai, black, 1, 1575129640000, 27, 10.231096
1, shanghai, black, 1, 1575129641000, 17, 12.611756
1, shanghai, black, 1, 1575129642000, 32, 13.148048
1, shanghai, black, 1, 1575129643000, 20, 18.997501
1, shanghai, black, 1, 1575129644000, 34, 11.001994
1, shanghai, black, 1, 1575129645000, 24, 17.698891
1, shanghai, black, 1, 1575129646000, 16, 12.623819
1, shanghai, black, 1, 1575129647000, 26, 12.146537
1, shanghai, black, 1, 1575129648000, 28, 13.511343
1, shanghai, black, 1, 1575129649000, 34, 15.783513
1, shanghai, black, 1, 1575129650000, 23, 11.198505
1, shanghai, black, 1, 1575129651000, 23, 10.537856
1, shanghai, black, 1, 1575129652000, 29, 13.241740
1, shanghai, black, 1, 1575129653000, 30, 13.492887
1, shanghai, black, 1, 1575129654000, 21, 19.687462
1, shanghai, black, 1, 1575129655000, 21, 12.079431
1, shanghai, black, 1, 1575129656000, 29, 13.022024
1, shanghai, black, 1, 1575129657000, 34, 11.340842
1, shanghai, black, 1, 1575129658000, 18, 16.408648
1, shanghai, black, 1, 1575129659000, 22, 18.098742
1, shanghai, black, 1, 1575129660000, 29, 19.427574
1, shanghai, black, 1, 1575129661000, 26, 14.946804
1, shanghai, black, 1, 1575129662000, 18, 17.107439
1, shanghai, black, 1, 1575129663000, 31, 14.076329
1, shanghai, black, 1, 1575129664000, 32, 19.443971
1, shanghai, black, 1, 1575129665000, 31, 12.886383
1, shanghai, black, 1, 1575129666000, 20, 14.525845
1, shanghai, black, 1, 1575129667000, 24, 13.153620
1, shanghai, black, 1, 1575129668000, 22, 17.515631
1, shanghai, black, 1, 1575129669000, 24, 16.697146
1, shanghai, black, 1, 1575129670000, 34, 14.588845
1, shanghai, black, 1, 1575129671000, 17, 14.815298
1, shanghai, black, 1, 1575129672000, 20, 19.506232
1, shanghai, black, 1, 1575129673000, 28, 17.425147
1, shanghai, black, 1, 1575129674000, 15, 10.661514
1, shanghai, black, 1, 1575129675000, 20, 19.254679
1, shanghai, black, 1, 1575129676000, 24, 14.094194
1, shanghai, black, 1, 1575129677000, 31, 10.972616
1, shanghai, black, 1, 1575129678000, 15, 10.044447
1, shanghai, black, 1, 1575129679000, 32, 11.093067
1, shanghai, black, 1, 1575129680000, 33, 12.570554
1, shanghai, black, 1, 1575129681000, 28, 19.264114
1, shanghai, black, 1, 1575129682000, 23, 13.038871
1, shanghai, black, 1, 1575129683000, 20, 11.764896
1, shanghai, black, 1, 1575129684000, 19, 17.051371
1, shanghai, black, 1, 1575129685000, 18, 12.503689
1, shanghai, black, 1, 1575129686000, 28, 17.512406
1, shanghai, black, 1, 1575129687000, 28, 18.409932
1, shanghai, black, 1, 1575129688000, 22, 10.132855
1, shanghai, black, 1, 1575129689000, 23, 18.993715
1, shanghai, black, 1, 1575129690000, 26, 10.430004
1, shanghai, black, 1, 1575129691000, 21, 10.510941
1, shanghai, black, 1, 1575129692000, 26, 14.756974
1, shanghai, black, 1, 1575129693000, 32, 10.407199
1, shanghai, black, 1, 1575129694000, 29, 12.601247
1, shanghai, black, 1, 1575129695000, 25, 19.604975
1, shanghai, black, 1, 1575129696000, 22, 12.293202
1, shanghai, black, 1, 1575129697000, 19, 17.564823
1, shanghai, black, 1, 1575129698000, 28, 13.389774
1, shanghai, black, 1, 1575129699000, 31, 19.859944
2, guangzhou, green, 2, 1575129600000, 17, 12.496550
2, guangzhou, green, 2, 1575129601000, 29, 17.897172
2, guangzhou, green, 2, 1575129602000, 34, 16.574690
2, guangzhou, green, 2, 1575129603000, 15, 16.575054
2, guangzhou, green, 2, 1575129604000, 34, 19.192545
2, guangzhou, green, 2, 1575129605000, 19, 15.203920
2, guangzhou, green, 2, 1575129606000, 28, 12.481825
2, guangzhou, green, 2, 1575129607000, 30, 16.997891
2, guangzhou, green, 2, 1575129608000, 24, 15.122720
2, guangzhou, green, 2, 1575129609000, 20, 16.220016
2, guangzhou, green, 2, 1575129610000, 16, 11.405753
2, guangzhou, green, 2, 1575129611000, 26, 19.440151
2, guangzhou, green, 2, 1575129612000, 24, 12.457920
2, guangzhou, green, 2, 1575129613000, 30, 15.369806
2, guangzhou, green, 2, 1575129614000, 27, 16.716676
2, guangzhou, green, 2, 1575129615000, 32, 17.338548
2, guangzhou, green, 2, 1575129616000, 28, 14.234738
2, guangzhou, green, 2, 1575129617000, 34, 19.530447
2, guangzhou, green, 2, 1575129618000, 15, 14.551896
2, guangzhou, green, 2, 1575129619000, 21, 17.198856
2, guangzhou, green, 2, 1575129620000, 19, 17.425909
2, guangzhou, green, 2, 1575129621000, 29, 16.825216
2, guangzhou, green, 2, 1575129622000, 28, 12.485828
2, guangzhou, green, 2, 1575129623000, 25, 17.699710
2, guangzhou, green, 2, 1575129624000, 30, 12.866378
2, guangzhou, green, 2, 1575129625000, 18, 11.985615
2, guangzhou, green, 2, 1575129626000, 24, 16.359533
2, guangzhou, green, 2, 1575129627000, 20, 14.123154
2, guangzhou, green, 2, 1575129628000, 23, 11.311899
2, guangzhou, green, 2, 1575129629000, 29, 18.450350
2, guangzhou, green, 2, 1575129630000, 29, 17.783038
2, guangzhou, green, 2, 1575129631000, 22, 16.543795
2, guangzhou, green, 2, 1575129632000, 25, 13.939652
2, guangzhou, green, 2, 1575129633000, 22, 15.658666
2, guangzhou, green, 2, 1575129634000, 24, 14.524828
2, guangzhou, green, 2, 1575129635000, 15, 16.428353
2, guangzhou, green, 2, 1575129636000, 16, 18.103802
2, guangzhou, green, 2, 1575129637000, 28, 10.814747
2, guangzhou, green, 2, 1575129638000, 21, 14.906347
2, guangzhou, green, 2, 1575129639000, 25, 16.276587
2, guangzhou, green, 2, 1575129640000, 28, 17.932145
2, guangzhou, green, 2, 1575129641000, 34, 12.543257
2, guangzhou, green, 2, 1575129642000, 21, 14.202174
2, guangzhou, green, 2, 1575129643000, 19, 12.169968
2, guangzhou, green, 2, 1575129644000, 31, 15.638443
2, guangzhou, green, 2, 1575129645000, 23, 13.675736
2, guangzhou, green, 2, 1575129646000, 20, 19.002998
2, guangzhou, green, 2, 1575129647000, 34, 14.451299
2, guangzhou, green, 2, 1575129648000, 29, 16.676133
2, guangzhou, green, 2, 1575129649000, 31, 10.066270
2, guangzhou, green, 2, 1575129650000, 26, 17.824551
2, guangzhou, green, 2, 1575129651000, 34, 18.082416
2, guangzhou, green, 2, 1575129652000, 28, 16.099497
2, guangzhou, green, 2, 1575129653000, 16, 12.265096
2, guangzhou, green, 2, 1575129654000, 34, 12.468646
2, guangzhou, green, 2, 1575129655000, 16, 11.534757
2, guangzhou, green, 2, 1575129656000, 16, 19.092035
2, guangzhou, green, 2, 1575129657000, 20, 13.272631
2, guangzhou, green, 2, 1575129658000, 19, 14.302918
2, guangzhou, green, 2, 1575129659000, 31, 10.996095
2, guangzhou, green, 2, 1575129660000, 17, 15.220791
2, guangzhou, green, 2, 1575129661000, 28, 18.482870
2, guangzhou, green, 2, 1575129662000, 17, 15.654042
2, guangzhou, green, 2, 1575129663000, 30, 12.753545
2, guangzhou, green, 2, 1575129664000, 18, 19.292998
2, guangzhou, green, 2, 1575129665000, 33, 12.108711
2, guangzhou, green, 2, 1575129666000, 34, 14.724292
2, guangzhou, green, 2, 1575129667000, 28, 13.754784
2, guangzhou, green, 2, 1575129668000, 22, 17.879010
2, guangzhou, green, 2, 1575129669000, 27, 10.963891
2, guangzhou, green, 2, 1575129670000, 32, 15.231074
2, guangzhou, green, 2, 1575129671000, 24, 11.802718
2, guangzhou, green, 2, 1575129672000, 21, 13.681011
2, guangzhou, green, 2, 1575129673000, 19, 10.910179
2, guangzhou, green, 2, 1575129674000, 29, 13.944866
2, guangzhou, green, 2, 1575129675000, 18, 17.558532
2, guangzhou, green, 2, 1575129676000, 19, 13.186824
2, guangzhou, green, 2, 1575129677000, 25, 12.784448
2, guangzhou, green, 2, 1575129678000, 28, 15.774681
2, guangzhou, green, 2, 1575129679000, 29, 11.104902
2, guangzhou, green, 2, 1575129680000, 16, 13.809837
2, guangzhou, green, 2, 1575129681000, 16, 18.830369
2, guangzhou, green, 2, 1575129682000, 32, 11.798459
2, guangzhou, green, 2, 1575129683000, 17, 11.893725
2, guangzhou, green, 2, 1575129684000, 16, 11.646352
2, guangzhou, green, 2, 1575129685000, 30, 16.511740
2, guangzhou, green, 2, 1575129686000, 27, 11.837594
2, guangzhou, green, 2, 1575129687000, 26, 17.312381
2, guangzhou, green, 2, 1575129688000, 16, 12.512595
2, guangzhou, green, 2, 1575129689000, 27, 10.224634
2, guangzhou, green, 2, 1575129690000, 31, 15.000720
2, guangzhou, green, 2, 1575129691000, 18, 12.810097
2, guangzhou, green, 2, 1575129692000, 24, 19.154830
2, guangzhou, green, 2, 1575129693000, 29, 17.029148
2, guangzhou, green, 2, 1575129694000, 25, 19.416777
2, guangzhou, green, 2, 1575129695000, 17, 17.692554
2, guangzhou, green, 2, 1575129696000, 25, 10.939226
2, guangzhou, green, 2, 1575129697000, 23, 10.632203
2, guangzhou, green, 2, 1575129698000, 21, 17.977449
2, guangzhou, green, 2, 1575129699000, 20, 14.047369
3, shenzhen, yellow, 0, 1575129600000, 17, 13.181688
3, shenzhen, yellow, 0, 1575129601000, 26, 17.912070
3, shenzhen, yellow, 0, 1575129602000, 28, 11.660286
3, shenzhen, yellow, 0, 1575129603000, 28, 16.496510
3, shenzhen, yellow, 0, 1575129604000, 32, 16.164662
3, shenzhen, yellow, 0, 1575129605000, 16, 19.604285
3, shenzhen, yellow, 0, 1575129606000, 33, 19.308120
3, shenzhen, yellow, 0, 1575129607000, 16, 16.755204
3, shenzhen, yellow, 0, 1575129608000, 33, 10.658284
3, shenzhen, yellow, 0, 1575129609000, 30, 17.241293
3, shenzhen, yellow, 0, 1575129610000, 16, 18.088522
3, shenzhen, yellow, 0, 1575129611000, 31, 15.455248
3, shenzhen, yellow, 0, 1575129612000, 29, 10.505713
3, shenzhen, yellow, 0, 1575129613000, 28, 16.189388
3, shenzhen, yellow, 0, 1575129614000, 16, 14.723009
3, shenzhen, yellow, 0, 1575129615000, 27, 15.670388
3, shenzhen, yellow, 0, 1575129616000, 29, 16.080214
3, shenzhen, yellow, 0, 1575129617000, 18, 18.544671
3, shenzhen, yellow, 0, 1575129618000, 23, 16.947663
3, shenzhen, yellow, 0, 1575129619000, 15, 16.917797
3, shenzhen, yellow, 0, 1575129620000, 25, 17.888324
3, shenzhen, yellow, 0, 1575129621000, 34, 18.520162
3, shenzhen, yellow, 0, 1575129622000, 29, 10.271190
3, shenzhen, yellow, 0, 1575129623000, 26, 11.781460
3, shenzhen, yellow, 0, 1575129624000, 16, 17.737292
3, shenzhen, yellow, 0, 1575129625000, 15, 13.730896
3, shenzhen, yellow, 0, 1575129626000, 28, 12.161647
3, shenzhen, yellow, 0, 1575129627000, 33, 15.012675
3, shenzhen, yellow, 0, 1575129628000, 28, 12.880752
3, shenzhen, yellow, 0, 1575129629000, 28, 12.418301
3, shenzhen, yellow, 0, 1575129630000, 16, 15.744831
3, shenzhen, yellow, 0, 1575129631000, 23, 10.551453
3, shenzhen, yellow, 0, 1575129632000, 32, 11.782227
3, shenzhen, yellow, 0, 1575129633000, 32, 16.531595
3, shenzhen, yellow, 0, 1575129634000, 19, 12.512090
3, shenzhen, yellow, 0, 1575129635000, 22, 16.554170
3, shenzhen, yellow, 0, 1575129636000, 20, 12.593234
3, shenzhen, yellow, 0, 1575129637000, 23, 10.267977
3, shenzhen, yellow, 0, 1575129638000, 19, 18.470475
3, shenzhen, yellow, 0, 1575129639000, 27, 11.479857
3, shenzhen, yellow, 0, 1575129640000, 29, 17.387964
3, shenzhen, yellow, 0, 1575129641000, 28, 18.605927
3, shenzhen, yellow, 0, 1575129642000, 28, 14.150780
3, shenzhen, yellow, 0, 1575129643000, 30, 12.112675
3, shenzhen, yellow, 0, 1575129644000, 20, 12.126206
3, shenzhen, yellow, 0, 1575129645000, 34, 11.627235
3, shenzhen, yellow, 0, 1575129646000, 34, 18.202179
3, shenzhen, yellow, 0, 1575129647000, 30, 12.447241
3, shenzhen, yellow, 0, 1575129648000, 15, 12.542049
3, shenzhen, yellow, 0, 1575129649000, 34, 12.043278
3, shenzhen, yellow, 0, 1575129650000, 26, 15.254272
3, shenzhen, yellow, 0, 1575129651000, 33, 14.655641
3, shenzhen, yellow, 0, 1575129652000, 21, 17.835511
3, shenzhen, yellow, 0, 1575129653000, 30, 18.979520
3, shenzhen, yellow, 0, 1575129654000, 26, 12.942195
3, shenzhen, yellow, 0, 1575129655000, 29, 19.775977
3, shenzhen, yellow, 0, 1575129656000, 31, 14.242160
3, shenzhen, yellow, 0, 1575129657000, 15, 10.568320
3, shenzhen, yellow, 0, 1575129658000, 21, 12.407690
3, shenzhen, yellow, 0, 1575129659000, 23, 14.165327
3, shenzhen, yellow, 0, 1575129660000, 27, 11.292074
3, shenzhen, yellow, 0, 1575129661000, 18, 11.011734
3, shenzhen, yellow, 0, 1575129662000, 22, 18.100115
3, shenzhen, yellow, 0, 1575129663000, 18, 11.857615
3, shenzhen, yellow, 0, 1575129664000, 20, 15.402887
3, shenzhen, yellow, 0, 1575129665000, 32, 17.952958
3, shenzhen, yellow, 0, 1575129666000, 16, 15.407510
3, shenzhen, yellow, 0, 1575129667000, 23, 17.344025
3, shenzhen, yellow, 0, 1575129668000, 34, 13.251864
3, shenzhen, yellow, 0, 1575129669000, 31, 15.406216
3, shenzhen, yellow, 0, 1575129670000, 19, 16.385551
3, shenzhen, yellow, 0, 1575129671000, 32, 13.493399
3, shenzhen, yellow, 0, 1575129672000, 27, 11.856057
3, shenzhen, yellow, 0, 1575129673000, 30, 12.977649
3, shenzhen, yellow, 0, 1575129674000, 19, 18.339123
3, shenzhen, yellow, 0, 1575129675000, 23, 16.442236
3, shenzhen, yellow, 0, 1575129676000, 18, 19.140272
3, shenzhen, yellow, 0, 1575129677000, 27, 16.562737
3, shenzhen, yellow, 0, 1575129678000, 16, 10.993309
3, shenzhen, yellow, 0, 1575129679000, 27, 15.137385
3, shenzhen, yellow, 0, 1575129680000, 15, 18.754543
3, shenzhen, yellow, 0, 1575129681000, 26, 10.116102
3, shenzhen, yellow, 0, 1575129682000, 29, 14.024587
3, shenzhen, yellow, 0, 1575129683000, 31, 14.016558
3, shenzhen, yellow, 0, 1575129684000, 19, 10.671284
3, shenzhen, yellow, 0, 1575129685000, 32, 14.641297
3, shenzhen, yellow, 0, 1575129686000, 18, 12.823655
3, shenzhen, yellow, 0, 1575129687000, 30, 19.260822
3, shenzhen, yellow, 0, 1575129688000, 30, 16.105202
3, shenzhen, yellow, 0, 1575129689000, 22, 10.230556
3, shenzhen, yellow, 0, 1575129690000, 17, 10.732315
3, shenzhen, yellow, 0, 1575129691000, 31, 15.320282
3, shenzhen, yellow, 0, 1575129692000, 24, 17.208577
3, shenzhen, yellow, 0, 1575129693000, 16, 12.506668
3, shenzhen, yellow, 0, 1575129694000, 17, 18.911875
3, shenzhen, yellow, 0, 1575129695000, 15, 12.665488
3, shenzhen, yellow, 0, 1575129696000, 18, 11.283357
3, shenzhen, yellow, 0, 1575129697000, 15, 13.186590
3, shenzhen, yellow, 0, 1575129698000, 34, 15.659293
3, shenzhen, yellow, 0, 1575129699000, 30, 12.898771
4, hangzhou, blue, 1, 1575129600000, 33, 18.262612
4, hangzhou, blue, 1, 1575129601000, 20, 11.612149
4, hangzhou, blue, 1, 1575129602000, 26, 17.261305
4, hangzhou, blue, 1, 1575129603000, 27, 19.240210
4, hangzhou, blue, 1, 1575129604000, 27, 17.412985
4, hangzhou, blue, 1, 1575129605000, 19, 12.835781
4, hangzhou, blue, 1, 1575129606000, 24, 13.087003
4, hangzhou, blue, 1, 1575129607000, 24, 13.701138
4, hangzhou, blue, 1, 1575129608000, 31, 10.076716
4, hangzhou, blue, 1, 1575129609000, 27, 14.703408
4, hangzhou, blue, 1, 1575129610000, 19, 17.503874
4, hangzhou, blue, 1, 1575129611000, 21, 18.607839
4, hangzhou, blue, 1, 1575129612000, 16, 15.416387
4, hangzhou, blue, 1, 1575129613000, 19, 19.477280
4, hangzhou, blue, 1, 1575129614000, 15, 17.374174
4, hangzhou, blue, 1, 1575129615000, 30, 10.732940
4, hangzhou, blue, 1, 1575129616000, 33, 16.863960
4, hangzhou, blue, 1, 1575129617000, 16, 10.413205
4, hangzhou, blue, 1, 1575129618000, 27, 14.130482
4, hangzhou, blue, 1, 1575129619000, 19, 10.731398
4, hangzhou, blue, 1, 1575129620000, 27, 11.713011
4, hangzhou, blue, 1, 1575129621000, 26, 19.063695
4, hangzhou, blue, 1, 1575129622000, 26, 16.309728
4, hangzhou, blue, 1, 1575129623000, 33, 12.229796
4, hangzhou, blue, 1, 1575129624000, 16, 15.176824
4, hangzhou, blue, 1, 1575129625000, 31, 12.417684
4, hangzhou, blue, 1, 1575129626000, 31, 17.284961
4, hangzhou, blue, 1, 1575129627000, 24, 12.530188
4, hangzhou, blue, 1, 1575129628000, 32, 15.067641
4, hangzhou, blue, 1, 1575129629000, 32, 18.546511
4, hangzhou, blue, 1, 1575129630000, 21, 13.049847
4, hangzhou, blue, 1, 1575129631000, 19, 17.509510
4, hangzhou, blue, 1, 1575129632000, 24, 13.289143
4, hangzhou, blue, 1, 1575129633000, 18, 19.179227
4, hangzhou, blue, 1, 1575129634000, 25, 18.128126
4, hangzhou, blue, 1, 1575129635000, 26, 19.627125
4, hangzhou, blue, 1, 1575129636000, 25, 16.090493
4, hangzhou, blue, 1, 1575129637000, 19, 19.093488
4, hangzhou, blue, 1, 1575129638000, 32, 17.563422
4, hangzhou, blue, 1, 1575129639000, 16, 12.867582
4, hangzhou, blue, 1, 1575129640000, 32, 11.606473
4, hangzhou, blue, 1, 1575129641000, 31, 12.321989
4, hangzhou, blue, 1, 1575129642000, 30, 17.043967
4, hangzhou, blue, 1, 1575129643000, 20, 14.553511
4, hangzhou, blue, 1, 1575129644000, 34, 19.068052
4, hangzhou, blue, 1, 1575129645000, 18, 15.992107
4, hangzhou, blue, 1, 1575129646000, 34, 11.308531
4, hangzhou, blue, 1, 1575129647000, 18, 19.053088
4, hangzhou, blue, 1, 1575129648000, 25, 18.617738
4, hangzhou, blue, 1, 1575129649000, 25, 14.190978
4, hangzhou, blue, 1, 1575129650000, 22, 18.049969
4, hangzhou, blue, 1, 1575129651000, 19, 16.890290
4, hangzhou, blue, 1, 1575129652000, 26, 10.055835
4, hangzhou, blue, 1, 1575129653000, 32, 18.772190
4, hangzhou, blue, 1, 1575129654000, 18, 15.347443
4, hangzhou, blue, 1, 1575129655000, 19, 15.611078
4, hangzhou, blue, 1, 1575129656000, 24, 11.345082
4, hangzhou, blue, 1, 1575129657000, 27, 10.883929
4, hangzhou, blue, 1, 1575129658000, 25, 19.810161
4, hangzhou, blue, 1, 1575129659000, 33, 10.159027
4, hangzhou, blue, 1, 1575129660000, 20, 11.900341
4, hangzhou, blue, 1, 1575129661000, 24, 12.395535
4, hangzhou, blue, 1, 1575129662000, 25, 13.832159
4, hangzhou, blue, 1, 1575129663000, 26, 15.066722
4, hangzhou, blue, 1, 1575129664000, 24, 12.441406
4, hangzhou, blue, 1, 1575129665000, 22, 16.281200
4, hangzhou, blue, 1, 1575129666000, 21, 14.116693
4, hangzhou, blue, 1, 1575129667000, 28, 12.441770
4, hangzhou, blue, 1, 1575129668000, 18, 11.402083
4, hangzhou, blue, 1, 1575129669000, 28, 15.167379
4, hangzhou, blue, 1, 1575129670000, 16, 15.433220
4, hangzhou, blue, 1, 1575129671000, 23, 10.211150
4, hangzhou, blue, 1, 1575129672000, 19, 19.501424
4, hangzhou, blue, 1, 1575129673000, 18, 17.974835
4, hangzhou, blue, 1, 1575129674000, 26, 12.904804
4, hangzhou, blue, 1, 1575129675000, 27, 17.012268
4, hangzhou, blue, 1, 1575129676000, 34, 11.223162
4, hangzhou, blue, 1, 1575129677000, 34, 11.008873
4, hangzhou, blue, 1, 1575129678000, 18, 13.466623
4, hangzhou, blue, 1, 1575129679000, 25, 11.714342
4, hangzhou, blue, 1, 1575129680000, 32, 15.193444
4, hangzhou, blue, 1, 1575129681000, 17, 13.998644
4, hangzhou, blue, 1, 1575129682000, 27, 12.180101
4, hangzhou, blue, 1, 1575129683000, 17, 16.405635
4, hangzhou, blue, 1, 1575129684000, 33, 16.027225
4, hangzhou, blue, 1, 1575129685000, 28, 17.864308
4, hangzhou, blue, 1, 1575129686000, 20, 16.057140
4, hangzhou, blue, 1, 1575129687000, 26, 17.240991
4, hangzhou, blue, 1, 1575129688000, 31, 11.178153
4, hangzhou, blue, 1, 1575129689000, 29, 11.688910
4, hangzhou, blue, 1, 1575129690000, 24, 15.830195
4, hangzhou, blue, 1, 1575129691000, 33, 13.083720
4, hangzhou, blue, 1, 1575129692000, 25, 15.003569
4, hangzhou, blue, 1, 1575129693000, 16, 14.412837
4, hangzhou, blue, 1, 1575129694000, 26, 18.930523
4, hangzhou, blue, 1, 1575129695000, 19, 10.657332
4, hangzhou, blue, 1, 1575129696000, 28, 11.193432
4, hangzhou, blue, 1, 1575129697000, 17, 18.000253
4, hangzhou, blue, 1, 1575129698000, 21, 15.908098
4, hangzhou, blue, 1, 1575129699000, 25, 14.506726
5, nanjing, white, 2, 1575129600000, 20, 17.327941
5, nanjing, white, 2, 1575129601000, 18, 14.271766
5, nanjing, white, 2, 1575129602000, 26, 19.593114
5, nanjing, white, 2, 1575129603000, 19, 13.142911
5, nanjing, white, 2, 1575129604000, 27, 15.166424
5, nanjing, white, 2, 1575129605000, 28, 11.804980
5, nanjing, white, 2, 1575129606000, 24, 17.625403
5, nanjing, white, 2, 1575129607000, 19, 11.373316
5, nanjing, white, 2, 1575129608000, 34, 19.434849
5, nanjing, white, 2, 1575129609000, 31, 14.078995
5, nanjing, white, 2, 1575129610000, 27, 11.647533
5, nanjing, white, 2, 1575129611000, 25, 16.624403
5, nanjing, white, 2, 1575129612000, 28, 12.862567
5, nanjing, white, 2, 1575129613000, 20, 18.218963
5, nanjing, white, 2, 1575129614000, 17, 10.021056
5, nanjing, white, 2, 1575129615000, 30, 16.042743
5, nanjing, white, 2, 1575129616000, 26, 11.424560
5, nanjing, white, 2, 1575129617000, 21, 10.094065
5, nanjing, white, 2, 1575129618000, 31, 15.982905
5, nanjing, white, 2, 1575129619000, 17, 15.925533
5, nanjing, white, 2, 1575129620000, 30, 15.622108
5, nanjing, white, 2, 1575129621000, 18, 19.320662
5, nanjing, white, 2, 1575129622000, 19, 14.068873
5, nanjing, white, 2, 1575129623000, 15, 15.213653
5, nanjing, white, 2, 1575129624000, 32, 16.028939
5, nanjing, white, 2, 1575129625000, 28, 17.858151
5, nanjing, white, 2, 1575129626000, 18, 11.261528
5, nanjing, white, 2, 1575129627000, 21, 10.262692
5, nanjing, white, 2, 1575129628000, 27, 13.190850
5, nanjing, white, 2, 1575129629000, 17, 15.404541
5, nanjing, white, 2, 1575129630000, 27, 10.852643
5, nanjing, white, 2, 1575129631000, 23, 13.134271
5, nanjing, white, 2, 1575129632000, 22, 19.928938
5, nanjing, white, 2, 1575129633000, 19, 10.683633
5, nanjing, white, 2, 1575129634000, 29, 15.450679
5, nanjing, white, 2, 1575129635000, 20, 17.032495
5, nanjing, white, 2, 1575129636000, 21, 16.081343
5, nanjing, white, 2, 1575129637000, 31, 15.173797
5, nanjing, white, 2, 1575129638000, 17, 18.062092
5, nanjing, white, 2, 1575129639000, 22, 14.139422
5, nanjing, white, 2, 1575129640000, 30, 15.335309
5, nanjing, white, 2, 1575129641000, 30, 18.381148
5, nanjing, white, 2, 1575129642000, 28, 15.640517
5, nanjing, white, 2, 1575129643000, 15, 10.603125
5, nanjing, white, 2, 1575129644000, 18, 12.096534
5, nanjing, white, 2, 1575129645000, 27, 17.015026
5, nanjing, white, 2, 1575129646000, 24, 15.616134
5, nanjing, white, 2, 1575129647000, 32, 15.552120
5, nanjing, white, 2, 1575129648000, 18, 13.846167
5, nanjing, white, 2, 1575129649000, 32, 15.406105
5, nanjing, white, 2, 1575129650000, 19, 14.396603
5, nanjing, white, 2, 1575129651000, 34, 15.660214
5, nanjing, white, 2, 1575129652000, 29, 19.035787
5, nanjing, white, 2, 1575129653000, 26, 14.746065
5, nanjing, white, 2, 1575129654000, 29, 14.144764
5, nanjing, white, 2, 1575129655000, 32, 11.953327
5, nanjing, white, 2, 1575129656000, 16, 11.546639
5, nanjing, white, 2, 1575129657000, 20, 12.779206
5, nanjing, white, 2, 1575129658000, 16, 16.364659
5, nanjing, white, 2, 1575129659000, 29, 10.204467
5, nanjing, white, 2, 1575129660000, 22, 18.824781
5, nanjing, white, 2, 1575129661000, 26, 18.795199
5, nanjing, white, 2, 1575129662000, 16, 12.142987
5, nanjing, white, 2, 1575129663000, 30, 13.810269
5, nanjing, white, 2, 1575129664000, 28, 19.670323
5, nanjing, white, 2, 1575129665000, 17, 10.776152
5, nanjing, white, 2, 1575129666000, 31, 18.095779
5, nanjing, white, 2, 1575129667000, 34, 12.720668
5, nanjing, white, 2, 1575129668000, 27, 18.285647
5, nanjing, white, 2, 1575129669000, 18, 15.929034
5, nanjing, white, 2, 1575129670000, 27, 10.397290
5, nanjing, white, 2, 1575129671000, 29, 12.914206
5, nanjing, white, 2, 1575129672000, 29, 11.560832
5, nanjing, white, 2, 1575129673000, 21, 15.487904
5, nanjing, white, 2, 1575129674000, 28, 11.585003
5, nanjing, white, 2, 1575129675000, 30, 15.042832
5, nanjing, white, 2, 1575129676000, 23, 12.408045
5, nanjing, white, 2, 1575129677000, 15, 17.353187
5, nanjing, white, 2, 1575129678000, 31, 18.084138
5, nanjing, white, 2, 1575129679000, 34, 10.756624
5, nanjing, white, 2, 1575129680000, 19, 13.270267
5, nanjing, white, 2, 1575129681000, 27, 16.639891
5, nanjing, white, 2, 1575129682000, 31, 14.671892
5, nanjing, white, 2, 1575129683000, 27, 10.554016
5, nanjing, white, 2, 1575129684000, 16, 14.507173
5, nanjing, white, 2, 1575129685000, 19, 11.977540
5, nanjing, white, 2, 1575129686000, 26, 13.286239
5, nanjing, white, 2, 1575129687000, 30, 17.858074
5, nanjing, white, 2, 1575129688000, 24, 19.446978
5, nanjing, white, 2, 1575129689000, 21, 19.698453
5, nanjing, white, 2, 1575129690000, 21, 19.494527
5, nanjing, white, 2, 1575129691000, 34, 11.911972
5, nanjing, white, 2, 1575129692000, 16, 16.283904
5, nanjing, white, 2, 1575129693000, 29, 12.346139
5, nanjing, white, 2, 1575129694000, 25, 10.589538
5, nanjing, white, 2, 1575129695000, 23, 16.730700
5, nanjing, white, 2, 1575129696000, 33, 16.858111
5, nanjing, white, 2, 1575129697000, 27, 13.779923
5, nanjing, white, 2, 1575129698000, 20, 11.035122
5, nanjing, white, 2, 1575129699000, 34, 10.444430
6, wuhan, black, 0, 1575129600000, 30, 13.948532
6, wuhan, black, 0, 1575129601000, 28, 12.860198
6, wuhan, black, 0, 1575129602000, 32, 14.979606
6, wuhan, black, 0, 1575129603000, 22, 11.844284
6, wuhan, black, 0, 1575129604000, 16, 19.507148
6, wuhan, black, 0, 1575129605000, 22, 14.315308
6, wuhan, black, 0, 1575129606000, 19, 13.773210
6, wuhan, black, 0, 1575129607000, 31, 18.224420
6, wuhan, black, 0, 1575129608000, 28, 15.962573
6, wuhan, black, 0, 1575129609000, 32, 12.855757
6, wuhan, black, 0, 1575129610000, 32, 11.010859
6, wuhan, black, 0, 1575129611000, 33, 11.110190
6, wuhan, black, 0, 1575129612000, 24, 18.628721
6, wuhan, black, 0, 1575129613000, 30, 16.044831
6, wuhan, black, 0, 1575129614000, 29, 14.617854
6, wuhan, black, 0, 1575129615000, 31, 15.591157
6, wuhan, black, 0, 1575129616000, 31, 12.486593
6, wuhan, black, 0, 1575129617000, 21, 17.680152
6, wuhan, black, 0, 1575129618000, 27, 10.341043
6, wuhan, black, 0, 1575129619000, 28, 13.359138
6, wuhan, black, 0, 1575129620000, 30, 19.654174
6, wuhan, black, 0, 1575129621000, 28, 18.037469
6, wuhan, black, 0, 1575129622000, 25, 18.404051
6, wuhan, black, 0, 1575129623000, 16, 14.856599
6, wuhan, black, 0, 1575129624000, 29, 19.552920
6, wuhan, black, 0, 1575129625000, 17, 13.434096
6, wuhan, black, 0, 1575129626000, 27, 17.019559
6, wuhan, black, 0, 1575129627000, 26, 15.173058
6, wuhan, black, 0, 1575129628000, 32, 12.826764
6, wuhan, black, 0, 1575129629000, 26, 17.535447
6, wuhan, black, 0, 1575129630000, 21, 14.249137
6, wuhan, black, 0, 1575129631000, 17, 17.047627
6, wuhan, black, 0, 1575129632000, 27, 16.650397
6, wuhan, black, 0, 1575129633000, 15, 13.081019
6, wuhan, black, 0, 1575129634000, 31, 16.957137
6, wuhan, black, 0, 1575129635000, 16, 14.120849
6, wuhan, black, 0, 1575129636000, 20, 19.559244
6, wuhan, black, 0, 1575129637000, 24, 17.951023
6, wuhan, black, 0, 1575129638000, 28, 12.034821
6, wuhan, black, 0, 1575129639000, 27, 19.410968
6, wuhan, black, 0, 1575129640000, 32, 19.163660
6, wuhan, black, 0, 1575129641000, 19, 18.268331
6, wuhan, black, 0, 1575129642000, 17, 13.487017
6, wuhan, black, 0, 1575129643000, 15, 19.085113
6, wuhan, black, 0, 1575129644000, 31, 18.786878
6, wuhan, black, 0, 1575129645000, 25, 17.901693
6, wuhan, black, 0, 1575129646000, 16, 13.458948
6, wuhan, black, 0, 1575129647000, 17, 16.372939
6, wuhan, black, 0, 1575129648000, 20, 16.547324
6, wuhan, black, 0, 1575129649000, 22, 14.801144
6, wuhan, black, 0, 1575129650000, 16, 15.819640
6, wuhan, black, 0, 1575129651000, 24, 16.569364
6, wuhan, black, 0, 1575129652000, 29, 13.750153
6, wuhan, black, 0, 1575129653000, 16, 14.846974
6, wuhan, black, 0, 1575129654000, 23, 15.937862
6, wuhan, black, 0, 1575129655000, 32, 19.969213
6, wuhan, black, 0, 1575129656000, 17, 16.589262
6, wuhan, black, 0, 1575129657000, 16, 15.983127
6, wuhan, black, 0, 1575129658000, 32, 19.981177
6, wuhan, black, 0, 1575129659000, 30, 15.526706
6, wuhan, black, 0, 1575129660000, 30, 11.473325
6, wuhan, black, 0, 1575129661000, 34, 14.734314
6, wuhan, black, 0, 1575129662000, 31, 19.298395
6, wuhan, black, 0, 1575129663000, 22, 16.150773
6, wuhan, black, 0, 1575129664000, 18, 10.211251
6, wuhan, black, 0, 1575129665000, 23, 16.773732
6, wuhan, black, 0, 1575129666000, 22, 14.005852
6, wuhan, black, 0, 1575129667000, 17, 13.159840
6, wuhan, black, 0, 1575129668000, 26, 13.747615
6, wuhan, black, 0, 1575129669000, 26, 14.601900
6, wuhan, black, 0, 1575129670000, 29, 10.489225
6, wuhan, black, 0, 1575129671000, 21, 16.890829
6, wuhan, black, 0, 1575129672000, 26, 11.081302
6, wuhan, black, 0, 1575129673000, 26, 19.336692
6, wuhan, black, 0, 1575129674000, 22, 13.601869
6, wuhan, black, 0, 1575129675000, 19, 11.627652
6, wuhan, black, 0, 1575129676000, 19, 13.767122
6, wuhan, black, 0, 1575129677000, 17, 15.320825
6, wuhan, black, 0, 1575129678000, 16, 13.546837
6, wuhan, black, 0, 1575129679000, 26, 19.562339
6, wuhan, black, 0, 1575129680000, 24, 18.861545
6, wuhan, black, 0, 1575129681000, 22, 11.048994
6, wuhan, black, 0, 1575129682000, 32, 18.633559
6, wuhan, black, 0, 1575129683000, 24, 11.423349
6, wuhan, black, 0, 1575129684000, 31, 10.958536
6, wuhan, black, 0, 1575129685000, 27, 16.700368
6, wuhan, black, 0, 1575129686000, 32, 19.383603
6, wuhan, black, 0, 1575129687000, 25, 12.817186
6, wuhan, black, 0, 1575129688000, 21, 19.289010
6, wuhan, black, 0, 1575129689000, 21, 18.514933
6, wuhan, black, 0, 1575129690000, 22, 19.214387
6, wuhan, black, 0, 1575129691000, 33, 11.673355
6, wuhan, black, 0, 1575129692000, 23, 18.321138
6, wuhan, black, 0, 1575129693000, 29, 11.371021
6, wuhan, black, 0, 1575129694000, 32, 10.531389
6, wuhan, black, 0, 1575129695000, 18, 15.921944
6, wuhan, black, 0, 1575129696000, 27, 16.780309
6, wuhan, black, 0, 1575129697000, 29, 12.028908
6, wuhan, black, 0, 1575129698000, 32, 14.714637
6, wuhan, black, 0, 1575129699000, 29, 12.753968
7, suzhou, green, 1, 1575129600000, 24, 15.501768
7, suzhou, green, 1, 1575129601000, 18, 17.583403
7, suzhou, green, 1, 1575129602000, 15, 14.919566
7, suzhou, green, 1, 1575129603000, 34, 11.870620
7, suzhou, green, 1, 1575129604000, 29, 13.098385
7, suzhou, green, 1, 1575129605000, 16, 17.498160
7, suzhou, green, 1, 1575129606000, 30, 19.744556
7, suzhou, green, 1, 1575129607000, 33, 16.558870
7, suzhou, green, 1, 1575129608000, 16, 12.532103
7, suzhou, green, 1, 1575129609000, 16, 16.504603
7, suzhou, green, 1, 1575129610000, 25, 11.681246
7, suzhou, green, 1, 1575129611000, 30, 10.620805
7, suzhou, green, 1, 1575129612000, 22, 16.687937
7, suzhou, green, 1, 1575129613000, 25, 17.911474
7, suzhou, green, 1, 1575129614000, 32, 11.036519
7, suzhou, green, 1, 1575129615000, 29, 16.162914
7, suzhou, green, 1, 1575129616000, 30, 10.425992
7, suzhou, green, 1, 1575129617000, 34, 19.630803
7, suzhou, green, 1, 1575129618000, 29, 17.739556
7, suzhou, green, 1, 1575129619000, 32, 17.805220
7, suzhou, green, 1, 1575129620000, 23, 15.547236
7, suzhou, green, 1, 1575129621000, 19, 13.928559
7, suzhou, green, 1, 1575129622000, 34, 15.063669
7, suzhou, green, 1, 1575129623000, 33, 16.968293
7, suzhou, green, 1, 1575129624000, 24, 17.425284
7, suzhou, green, 1, 1575129625000, 29, 12.856950
7, suzhou, green, 1, 1575129626000, 16, 10.769358
7, suzhou, green, 1, 1575129627000, 19, 19.106196
7, suzhou, green, 1, 1575129628000, 15, 18.987306
7, suzhou, green, 1, 1575129629000, 18, 19.311755
7, suzhou, green, 1, 1575129630000, 20, 11.854711
7, suzhou, green, 1, 1575129631000, 17, 11.268703
7, suzhou, green, 1, 1575129632000, 28, 18.451425
7, suzhou, green, 1, 1575129633000, 30, 15.813294
7, suzhou, green, 1, 1575129634000, 28, 14.549649
7, suzhou, green, 1, 1575129635000, 30, 18.777474
7, suzhou, green, 1, 1575129636000, 28, 18.789080
7, suzhou, green, 1, 1575129637000, 22, 12.038230
7, suzhou, green, 1, 1575129638000, 15, 10.294816
7, suzhou, green, 1, 1575129639000, 18, 19.396735
7, suzhou, green, 1, 1575129640000, 20, 17.763178
7, suzhou, green, 1, 1575129641000, 27, 17.413355
7, suzhou, green, 1, 1575129642000, 29, 12.723483
7, suzhou, green, 1, 1575129643000, 29, 12.753222
7, suzhou, green, 1, 1575129644000, 25, 11.097518
7, suzhou, green, 1, 1575129645000, 27, 15.300300
7, suzhou, green, 1, 1575129646000, 34, 11.625943
7, suzhou, green, 1, 1575129647000, 25, 16.646308
7, suzhou, green, 1, 1575129648000, 31, 10.940592
7, suzhou, green, 1, 1575129649000, 25, 18.853796
7, suzhou, green, 1, 1575129650000, 23, 16.183418
7, suzhou, green, 1, 1575129651000, 34, 15.379113
7, suzhou, green, 1, 1575129652000, 15, 10.424659
7, suzhou, green, 1, 1575129653000, 25, 10.196040
7, suzhou, green, 1, 1575129654000, 24, 15.591199
7, suzhou, green, 1, 1575129655000, 31, 17.032220
7, suzhou, green, 1, 1575129656000, 30, 14.349576
7, suzhou, green, 1, 1575129657000, 21, 14.315072
7, suzhou, green, 1, 1575129658000, 18, 12.297491
7, suzhou, green, 1, 1575129659000, 27, 13.134474
7, suzhou, green, 1, 1575129660000, 28, 16.510527
7, suzhou, green, 1, 1575129661000, 21, 17.905938
7, suzhou, green, 1, 1575129662000, 16, 14.310720
7, suzhou, green, 1, 1575129663000, 33, 12.415139
7, suzhou, green, 1, 1575129664000, 28, 19.899145
7, suzhou, green, 1, 1575129665000, 32, 18.874009
7, suzhou, green, 1, 1575129666000, 34, 16.834873
7, suzhou, green, 1, 1575129667000, 16, 18.383447
7, suzhou, green, 1, 1575129668000, 29, 11.365641
7, suzhou, green, 1, 1575129669000, 34, 13.137474
7, suzhou, green, 1, 1575129670000, 18, 13.566243
7, suzhou, green, 1, 1575129671000, 27, 16.454975
7, suzhou, green, 1, 1575129672000, 21, 10.957562
7, suzhou, green, 1, 1575129673000, 24, 14.916977
7, suzhou, green, 1, 1575129674000, 28, 12.449565
7, suzhou, green, 1, 1575129675000, 20, 10.217084
7, suzhou, green, 1, 1575129676000, 32, 15.026526
7, suzhou, green, 1, 1575129677000, 20, 10.291223
7, suzhou, green, 1, 1575129678000, 24, 13.561227
7, suzhou, green, 1, 1575129679000, 26, 10.091348
7, suzhou, green, 1, 1575129680000, 25, 13.574391
7, suzhou, green, 1, 1575129681000, 33, 17.308216
7, suzhou, green, 1, 1575129682000, 15, 11.635235
7, suzhou, green, 1, 1575129683000, 31, 19.967076
7, suzhou, green, 1, 1575129684000, 25, 11.849431
7, suzhou, green, 1, 1575129685000, 31, 16.161484
7, suzhou, green, 1, 1575129686000, 20, 15.716389
7, suzhou, green, 1, 1575129687000, 22, 17.486091
7, suzhou, green, 1, 1575129688000, 29, 10.390956
7, suzhou, green, 1, 1575129689000, 18, 18.549987
7, suzhou, green, 1, 1575129690000, 21, 12.367505
7, suzhou, green, 1, 1575129691000, 30, 12.345558
7, suzhou, green, 1, 1575129692000, 17, 14.100245
7, suzhou, green, 1, 1575129693000, 19, 11.093554
7, suzhou, green, 1, 1575129694000, 26, 13.614985
7, suzhou, green, 1, 1575129695000, 28, 13.753683
7, suzhou, green, 1, 1575129696000, 21, 12.691688
7, suzhou, green, 1, 1575129697000, 29, 17.595583
7, suzhou, green, 1, 1575129698000, 20, 13.184472
7, suzhou, green, 1, 1575129699000, 17, 14.349156
8, haerbing, yellow, 2, 1575129600000, 28, 13.254039
8, haerbing, yellow, 2, 1575129601000, 21, 17.815564
8, haerbing, yellow, 2, 1575129602000, 19, 11.209747
8, haerbing, yellow, 2, 1575129603000, 26, 16.861074
8, haerbing, yellow, 2, 1575129604000, 31, 11.504868
8, haerbing, yellow, 2, 1575129605000, 34, 19.224629
8, haerbing, yellow, 2, 1575129606000, 23, 11.358596
8, haerbing, yellow, 2, 1575129607000, 31, 12.635280
8, haerbing, yellow, 2, 1575129608000, 26, 11.433395
8, haerbing, yellow, 2, 1575129609000, 17, 13.468466
8, haerbing, yellow, 2, 1575129610000, 33, 14.519953
8, haerbing, yellow, 2, 1575129611000, 15, 14.241436
8, haerbing, yellow, 2, 1575129612000, 16, 13.055456
8, haerbing, yellow, 2, 1575129613000, 17, 13.772431
8, haerbing, yellow, 2, 1575129614000, 19, 12.057286
8, haerbing, yellow, 2, 1575129615000, 19, 13.647710
8, haerbing, yellow, 2, 1575129616000, 20, 15.103685
8, haerbing, yellow, 2, 1575129617000, 18, 16.627761
8, haerbing, yellow, 2, 1575129618000, 26, 18.441795
8, haerbing, yellow, 2, 1575129619000, 15, 18.348824
8, haerbing, yellow, 2, 1575129620000, 32, 18.431012
8, haerbing, yellow, 2, 1575129621000, 17, 10.795047
8, haerbing, yellow, 2, 1575129622000, 34, 10.793828
8, haerbing, yellow, 2, 1575129623000, 18, 16.664458
8, haerbing, yellow, 2, 1575129624000, 22, 16.533227
8, haerbing, yellow, 2, 1575129625000, 15, 12.870278
8, haerbing, yellow, 2, 1575129626000, 31, 17.592231
8, haerbing, yellow, 2, 1575129627000, 17, 10.092316
8, haerbing, yellow, 2, 1575129628000, 22, 10.988946
8, haerbing, yellow, 2, 1575129629000, 17, 14.493579
8, haerbing, yellow, 2, 1575129630000, 20, 11.943546
8, haerbing, yellow, 2, 1575129631000, 28, 19.871601
8, haerbing, yellow, 2, 1575129632000, 16, 16.607235
8, haerbing, yellow, 2, 1575129633000, 19, 10.197650
8, haerbing, yellow, 2, 1575129634000, 19, 10.742104
8, haerbing, yellow, 2, 1575129635000, 30, 18.785863
8, haerbing, yellow, 2, 1575129636000, 16, 14.827333
8, haerbing, yellow, 2, 1575129637000, 28, 13.826542
8, haerbing, yellow, 2, 1575129638000, 16, 18.638533
8, haerbing, yellow, 2, 1575129639000, 24, 17.832974
8, haerbing, yellow, 2, 1575129640000, 31, 14.904558
8, haerbing, yellow, 2, 1575129641000, 32, 16.034774
8, haerbing, yellow, 2, 1575129642000, 33, 16.879997
8, haerbing, yellow, 2, 1575129643000, 18, 16.981511
8, haerbing, yellow, 2, 1575129644000, 19, 18.554924
8, haerbing, yellow, 2, 1575129645000, 28, 12.138742
8, haerbing, yellow, 2, 1575129646000, 27, 17.938497
8, haerbing, yellow, 2, 1575129647000, 25, 16.919425
8, haerbing, yellow, 2, 1575129648000, 15, 17.739521
8, haerbing, yellow, 2, 1575129649000, 26, 16.017035
8, haerbing, yellow, 2, 1575129650000, 20, 14.530903
8, haerbing, yellow, 2, 1575129651000, 32, 10.938258
8, haerbing, yellow, 2, 1575129652000, 18, 15.265134
8, haerbing, yellow, 2, 1575129653000, 25, 11.227825
8, haerbing, yellow, 2, 1575129654000, 32, 15.839538
8, haerbing, yellow, 2, 1575129655000, 20, 12.813906
8, haerbing, yellow, 2, 1575129656000, 34, 14.348205
8, haerbing, yellow, 2, 1575129657000, 23, 13.158134
8, haerbing, yellow, 2, 1575129658000, 27, 18.320920
8, haerbing, yellow, 2, 1575129659000, 31, 10.848533
8, haerbing, yellow, 2, 1575129660000, 21, 13.549193
8, haerbing, yellow, 2, 1575129661000, 21, 10.043014
8, haerbing, yellow, 2, 1575129662000, 17, 13.852666
8, haerbing, yellow, 2, 1575129663000, 20, 13.046154
8, haerbing, yellow, 2, 1575129664000, 15, 15.538251
8, haerbing, yellow, 2, 1575129665000, 25, 15.422191
8, haerbing, yellow, 2, 1575129666000, 23, 17.912156
8, haerbing, yellow, 2, 1575129667000, 31, 10.870706
8, haerbing, yellow, 2, 1575129668000, 15, 15.348852
8, haerbing, yellow, 2, 1575129669000, 15, 19.605174
8, haerbing, yellow, 2, 1575129670000, 20, 12.633162
8, haerbing, yellow, 2, 1575129671000, 23, 15.347140
8, haerbing, yellow, 2, 1575129672000, 23, 19.131427
8, haerbing, yellow, 2, 1575129673000, 28, 17.031277
8, haerbing, yellow, 2, 1575129674000, 25, 12.871234
8, haerbing, yellow, 2, 1575129675000, 27, 12.112865
8, haerbing, yellow, 2, 1575129676000, 28, 14.989160
8, haerbing, yellow, 2, 1575129677000, 34, 12.925199
8, haerbing, yellow, 2, 1575129678000, 30, 11.244869
8, haerbing, yellow, 2, 1575129679000, 34, 13.189385
8, haerbing, yellow, 2, 1575129680000, 32, 12.347545
8, haerbing, yellow, 2, 1575129681000, 29, 14.551418
8, haerbing, yellow, 2, 1575129682000, 30, 14.502223
8, haerbing, yellow, 2, 1575129683000, 32, 13.304706
8, haerbing, yellow, 2, 1575129684000, 25, 12.030741
8, haerbing, yellow, 2, 1575129685000, 17, 16.387617
8, haerbing, yellow, 2, 1575129686000, 15, 19.766795
8, haerbing, yellow, 2, 1575129687000, 21, 16.533866
8, haerbing, yellow, 2, 1575129688000, 17, 11.657003
8, haerbing, yellow, 2, 1575129689000, 34, 12.667008
8, haerbing, yellow, 2, 1575129690000, 22, 15.673815
8, haerbing, yellow, 2, 1575129691000, 22, 15.767975
8, haerbing, yellow, 2, 1575129692000, 31, 19.982548
8, haerbing, yellow, 2, 1575129693000, 29, 19.036149
8, haerbing, yellow, 2, 1575129694000, 24, 16.044736
8, haerbing, yellow, 2, 1575129695000, 19, 12.138802
8, haerbing, yellow, 2, 1575129696000, 28, 17.771396
8, haerbing, yellow, 2, 1575129697000, 31, 16.321497
8, haerbing, yellow, 2, 1575129698000, 25, 15.864515
8, haerbing, yellow, 2, 1575129699000, 25, 16.492443
9, sijiazhuang, blue, 0, 1575129600000, 23, 16.002889
9, sijiazhuang, blue, 0, 1575129601000, 26, 17.034610
9, sijiazhuang, blue, 0, 1575129602000, 29, 12.892319
9, sijiazhuang, blue, 0, 1575129603000, 34, 15.321807
9, sijiazhuang, blue, 0, 1575129604000, 29, 12.562642
9, sijiazhuang, blue, 0, 1575129605000, 32, 17.190246
9, sijiazhuang, blue, 0, 1575129606000, 19, 15.361774
9, sijiazhuang, blue, 0, 1575129607000, 26, 15.022364
9, sijiazhuang, blue, 0, 1575129608000, 31, 14.837084
9, sijiazhuang, blue, 0, 1575129609000, 25, 11.554289
9, sijiazhuang, blue, 0, 1575129610000, 21, 15.313973
9, sijiazhuang, blue, 0, 1575129611000, 27, 18.621783
9, sijiazhuang, blue, 0, 1575129612000, 31, 18.018101
9, sijiazhuang, blue, 0, 1575129613000, 23, 14.421450
9, sijiazhuang, blue, 0, 1575129614000, 28, 10.833142
9, sijiazhuang, blue, 0, 1575129615000, 33, 18.169837
9, sijiazhuang, blue, 0, 1575129616000, 21, 18.772730
9, sijiazhuang, blue, 0, 1575129617000, 24, 18.893146
9, sijiazhuang, blue, 0, 1575129618000, 24, 10.290187
9, sijiazhuang, blue, 0, 1575129619000, 23, 17.393345
9, sijiazhuang, blue, 0, 1575129620000, 30, 12.949215
9, sijiazhuang, blue, 0, 1575129621000, 19, 19.267621
9, sijiazhuang, blue, 0, 1575129622000, 33, 14.831735
9, sijiazhuang, blue, 0, 1575129623000, 21, 14.711125
9, sijiazhuang, blue, 0, 1575129624000, 16, 17.168485
9, sijiazhuang, blue, 0, 1575129625000, 17, 16.426433
9, sijiazhuang, blue, 0, 1575129626000, 19, 13.879050
9, sijiazhuang, blue, 0, 1575129627000, 21, 18.308168
9, sijiazhuang, blue, 0, 1575129628000, 17, 10.845681
9, sijiazhuang, blue, 0, 1575129629000, 20, 10.238272
9, sijiazhuang, blue, 0, 1575129630000, 19, 19.424976
9, sijiazhuang, blue, 0, 1575129631000, 31, 13.885909
9, sijiazhuang, blue, 0, 1575129632000, 15, 19.264740
9, sijiazhuang, blue, 0, 1575129633000, 30, 12.460645
9, sijiazhuang, blue, 0, 1575129634000, 27, 17.608036
9, sijiazhuang, blue, 0, 1575129635000, 25, 13.493812
9, sijiazhuang, blue, 0, 1575129636000, 19, 10.955939
9, sijiazhuang, blue, 0, 1575129637000, 24, 11.956587
9, sijiazhuang, blue, 0, 1575129638000, 15, 19.141381
9, sijiazhuang, blue, 0, 1575129639000, 24, 14.801530
9, sijiazhuang, blue, 0, 1575129640000, 17, 14.347318
9, sijiazhuang, blue, 0, 1575129641000, 29, 14.803237
9, sijiazhuang, blue, 0, 1575129642000, 28, 10.342297
9, sijiazhuang, blue, 0, 1575129643000, 29, 19.368282
9, sijiazhuang, blue, 0, 1575129644000, 31, 17.491654
9, sijiazhuang, blue, 0, 1575129645000, 18, 13.161736
9, sijiazhuang, blue, 0, 1575129646000, 17, 16.067354
9, sijiazhuang, blue, 0, 1575129647000, 18, 13.736465
9, sijiazhuang, blue, 0, 1575129648000, 23, 19.103276
9, sijiazhuang, blue, 0, 1575129649000, 29, 16.075892
9, sijiazhuang, blue, 0, 1575129650000, 21, 10.728566
9, sijiazhuang, blue, 0, 1575129651000, 15, 18.921849
9, sijiazhuang, blue, 0, 1575129652000, 24, 16.914709
9, sijiazhuang, blue, 0, 1575129653000, 19, 13.501651
9, sijiazhuang, blue, 0, 1575129654000, 19, 13.538347
9, sijiazhuang, blue, 0, 1575129655000, 16, 13.261095
9, sijiazhuang, blue, 0, 1575129656000, 32, 16.315746
9, sijiazhuang, blue, 0, 1575129657000, 27, 16.400939
9, sijiazhuang, blue, 0, 1575129658000, 24, 13.321819
9, sijiazhuang, blue, 0, 1575129659000, 27, 19.070181
9, sijiazhuang, blue, 0, 1575129660000, 27, 13.040922
9, sijiazhuang, blue, 0, 1575129661000, 32, 10.872530
9, sijiazhuang, blue, 0, 1575129662000, 28, 16.428657
9, sijiazhuang, blue, 0, 1575129663000, 32, 13.883854
9, sijiazhuang, blue, 0, 1575129664000, 33, 14.299554
9, sijiazhuang, blue, 0, 1575129665000, 30, 16.445130
9, sijiazhuang, blue, 0, 1575129666000, 15, 18.059404
9, sijiazhuang, blue, 0, 1575129667000, 21, 12.348847
9, sijiazhuang, blue, 0, 1575129668000, 32, 13.315378
9, sijiazhuang, blue, 0, 1575129669000, 17, 15.689507
9, sijiazhuang, blue, 0, 1575129670000, 22, 15.591808
9, sijiazhuang, blue, 0, 1575129671000, 27, 16.386065
9, sijiazhuang, blue, 0, 1575129672000, 25, 10.564803
9, sijiazhuang, blue, 0, 1575129673000, 20, 12.276544
9, sijiazhuang, blue, 0, 1575129674000, 26, 15.828786
9, sijiazhuang, blue, 0, 1575129675000, 18, 12.236420
9, sijiazhuang, blue, 0, 1575129676000, 15, 19.439522
9, sijiazhuang, blue, 0, 1575129677000, 19, 19.831531
9, sijiazhuang, blue, 0, 1575129678000, 22, 17.115744
9, sijiazhuang, blue, 0, 1575129679000, 29, 19.879456
9, sijiazhuang, blue, 0, 1575129680000, 34, 10.207136
9, sijiazhuang, blue, 0, 1575129681000, 16, 17.633523
9, sijiazhuang, blue, 0, 1575129682000, 15, 14.227873
9, sijiazhuang, blue, 0, 1575129683000, 34, 12.027768
9, sijiazhuang, blue, 0, 1575129684000, 22, 11.376610
9, sijiazhuang, blue, 0, 1575129685000, 21, 11.711299
9, sijiazhuang, blue, 0, 1575129686000, 33, 14.281126
9, sijiazhuang, blue, 0, 1575129687000, 31, 10.895302
9, sijiazhuang, blue, 0, 1575129688000, 31, 13.971350
9, sijiazhuang, blue, 0, 1575129689000, 15, 15.262790
9, sijiazhuang, blue, 0, 1575129690000, 23, 12.440568
9, sijiazhuang, blue, 0, 1575129691000, 32, 19.731267
9, sijiazhuang, blue, 0, 1575129692000, 22, 10.518092
9, sijiazhuang, blue, 0, 1575129693000, 34, 17.863021
9, sijiazhuang, blue, 0, 1575129694000, 28, 11.478909
9, sijiazhuang, blue, 0, 1575129695000, 16, 15.075524
9, sijiazhuang, blue, 0, 1575129696000, 16, 10.292127
9, sijiazhuang, blue, 0, 1575129697000, 22, 13.716012
9, sijiazhuang, blue, 0, 1575129698000, 32, 10.906551
9, sijiazhuang, blue, 0, 1575129699000, 19, 18.386868
\ No newline at end of file
importSampleData/import/import_config.go 0 → 100644
浏览文件 @ f96c0195
package dataimport
import (
"encoding/json"
"fmt"
"path/filepath"
"sync"
"github.com/pelletier/go-toml"
)
var (
cfg Config
once sync.Once
)
// Config inclue all scene import config
type Config struct {
UserCases map[string]CaseConfig
}
// CaseConfig include the sample data config and tdengine config
type CaseConfig struct {
Format string
FilePath string
Separator string
Stname string
SubTableName string
Timestamp string
TimestampType string
TimestampTypeFormat string
Tags []FieldInfo
Fields []FieldInfo
}
// FieldInfo is field or tag info
type FieldInfo struct {
Name string
Type string
}
// LoadConfig will load the specified file config
func LoadConfig(filePath string) Config {
once.Do(func() {
filePath, err := filepath.Abs(filePath)
if err != nil {
panic(err)
}
fmt.Printf("parse toml file once. filePath: %s\n", filePath)
tree, err := toml.LoadFile(filePath)
if err != nil {
panic(err)
}
bytes, err := json.Marshal(tree.ToMap())
if err != nil {
panic(err)
}
err = json.Unmarshal(bytes, &cfg.UserCases)
if err != nil {
panic(err)
}
})
return cfg
}
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