Python-3-basics-series | http : | Time Series Database library

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Python-3-basics-series is a Python library typically used in Database, Time Series Database applications. Python-3-basics-series has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Python-3-basics-series build file is not available. You can download it from GitHub.
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                                                                                  Community Discussions

                                                                                  Trending Discussions on Time Series Database

                                                                                  How do I instrument region and environment information correctly in Prometheus?
                                                                                  chevron right
                                                                                  Amazon EKS (NFS) to Kubernetes pod. Can't mount volume
                                                                                  chevron right
                                                                                  InfluxDB not starting: 8086 bind address already in use
                                                                                  chevron right
                                                                                  Writing the data to the timeseries database over unstable network
                                                                                  chevron right
                                                                                  Recommended approach to store multi-dimensional data (e.g. spectra) in InfluxDB
                                                                                  chevron right
                                                                                  What to report in a time serie database when the measure failed?
                                                                                  chevron right
                                                                                  R ggplot customize month labels in time series
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                                                                                  How Can I Generate A Visualisation with Multiple Data Series In Splunk
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                                                                                  How can I deploy QuestDB on GCP?
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                                                                                  Group By day for custom time interval
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                                                                                  QUESTION

                                                                                  How do I instrument region and environment information correctly in Prometheus?
                                                                                  Asked 2022-Mar-09 at 17:53

                                                                                  I've an application, and I'm running one instance of this application per AWS region. I'm trying to instrument the application code with Prometheus metrics client, and will be exposing the collected metrics to the /metrics endpoint. There is a central server which will scrape the /metrics endpoints across all the regions and will store them in a central Time Series Database.

                                                                                  Let's say I've defined a metric named: http_responses_total then I would like to know its value aggregated over all the regions along with individual regional values. How do I store this region information which could be any one of the 13 regions and env information which could be dev or test or prod along with metrics so that I can slice and dice metrics based on region and env?

                                                                                  I found a few ways to do it, but not sure how it's done in general, as it seems a pretty common scenario:

                                                                                  I'm new to Prometheus. Could someone please suggest how I should store this region and env information? Are there any other better ways?

                                                                                  ANSWER

                                                                                  Answered 2022-Mar-09 at 17:53

                                                                                  All the proposed options will work, and all of them have downsides.

                                                                                  The first option (having env and region exposed by the application with every metric) is easy to implement but hard to maintain. Eventually somebody will forget to about these, opening a possibility for an unobserved failure to occur. Aside from that, you may not be able to add these labels to other exporters, written by someone else. Lastly, if you have to deal with millions of time series, more plain text data means more traffic.

                                                                                  The third option (storing these labels in a separate metric) will make it quite difficult to write and understand queries. Take this one for example:

                                                                                  sum by(instance) (node_arp_entries) and on(instance) node_exporter_build_info{version="0.17.0"}
                                                                                  

                                                                                  It calculates a sum of node_arp_entries for instances with node-exporter version="0.17.0". Well more specifically it calculates a sum for every instance and then just drops those with a wrong version, but you got the idea.

                                                                                  The second option (adding these labels with Prometheus as a part of scrape configuration) is what I would choose. To save the words, consider this monitoring setup:

                                                                                  Datacener Prometheus Regional Prometheus Global Prometheus 1. Collects metrics from local instances. 2. Adds dc label to each metric. 3. Pushes the data into the regional Prometheus -> 1. Collects data on datacenter scale. 2. Adds region label to all metrics. 3. Pushes the data into the global instance -> Simply collects and stores the data on global scale

                                                                                  This is the kind of setup you need on Google scale, but the point is the simplicity. It's perfectly clear where each label comes from and why. This approach requires you to make Prometheus configuration somewhat more complicated, and the less Prometheus instances you have, the more scrape configurations you will need. Overall, I think, this option beats the alternatives.

                                                                                  Source https://stackoverflow.com/questions/71408188

                                                                                  QUESTION

                                                                                  Amazon EKS (NFS) to Kubernetes pod. Can't mount volume
                                                                                  Asked 2021-Nov-10 at 02:26

                                                                                  I'm working on attaching Amazon EKS (NFS) to Kubernetes pod using terraform.

                                                                                  Everything runs without an error and is created:

                                                                                  • Pod victoriametrics
                                                                                  • Storage Classes
                                                                                  • Persistent Volumes
                                                                                  • Persistent Volume Claims

                                                                                  However, the volume victoriametrics-data doesn't attach to the pod. Anyway, I can't see one in the pod's shell. Could someone be so kind to help me understand where I'm wrong, please?

                                                                                  I have cut some unimportant code for the question to get code shorted.

                                                                                  resource "kubernetes_deployment" "victoriametrics" {
                                                                                  ...
                                                                                        spec {
                                                                                          container {
                                                                                            image = var.image
                                                                                            name  = var.name
                                                                                            ...
                                                                                            volume_mount {
                                                                                                mount_path        = "/data"
                                                                                                mount_propagation = "None"
                                                                                                name              = "victoriametrics-data"
                                                                                                read_only         = false
                                                                                              }
                                                                                          }
                                                                                  
                                                                                          volume {
                                                                                              name = "victoriametrics-data"
                                                                                          }
                                                                                  
                                                                                        }
                                                                                      }
                                                                                  ...
                                                                                  
                                                                                  }
                                                                                  
                                                                                  resource "kubernetes_csi_driver" "efs" {
                                                                                    metadata {
                                                                                      name = "${local.cluster_name}-${local.namespace}"
                                                                                      annotations = {
                                                                                        name = "For store data of ${local.namespace}."
                                                                                      }
                                                                                    }
                                                                                    spec {
                                                                                      attach_required        = true
                                                                                      pod_info_on_mount      = true
                                                                                      volume_lifecycle_modes = ["Persistent"]
                                                                                    }
                                                                                  }
                                                                                  
                                                                                  resource "kubernetes_storage_class" "efs" {
                                                                                    metadata {
                                                                                      name = "efs-sc"
                                                                                    }
                                                                                    storage_provisioner = kubernetes_csi_driver.efs.id
                                                                                    reclaim_policy      = "Retain"
                                                                                    mount_options       = ["file_mode=0700", "dir_mode=0777", "mfsymlinks", "uid=1000", "gid=1000", "nobrl", "cache=none"]
                                                                                  }
                                                                                  
                                                                                  resource "kubernetes_persistent_volume" "victoriametrics" {
                                                                                    metadata {
                                                                                      name = "${local.cluster_name}-${local.namespace}"
                                                                                    }
                                                                                    spec {
                                                                                      storage_class_name               = "efs-sc"
                                                                                      persistent_volume_reclaim_policy = "Retain"
                                                                                      volume_mode                      = "Filesystem"
                                                                                      access_modes                     = ["ReadWriteMany"]
                                                                                      capacity = {
                                                                                        storage = var.size_of_persistent_volume_claim
                                                                                      }
                                                                                      persistent_volume_source {
                                                                                        nfs {
                                                                                          path   = "/"
                                                                                          server = local.eks_iput_target
                                                                                        }
                                                                                      }
                                                                                    }
                                                                                  }
                                                                                  
                                                                                  resource "kubernetes_persistent_volume_claim" "victoriametrics" {
                                                                                    metadata {
                                                                                      name      = local.name_persistent_volume_claim
                                                                                      namespace = local.namespace
                                                                                    }
                                                                                    spec {
                                                                                      access_modes       = ["ReadWriteMany"]
                                                                                      storage_class_name = "efs-sc"
                                                                                      resources {
                                                                                        requests = {
                                                                                          storage = var.size_of_persistent_volume_claim
                                                                                        }
                                                                                      }
                                                                                      volume_name = kubernetes_persistent_volume.victoriametrics.metadata.0.name
                                                                                    }
                                                                                  }
                                                                                  
                                                                                  kind: Deployment
                                                                                  apiVersion: apps/v1
                                                                                  metadata:
                                                                                    name: victoriametrics
                                                                                    namespace: victoriametrics
                                                                                    labels:
                                                                                      k8s-app: victoriametrics
                                                                                      purpose: victoriametrics
                                                                                    annotations:
                                                                                      deployment.kubernetes.io/revision: '1'
                                                                                      name: >-
                                                                                        VictoriaMetrics - The High Performance Open Source Time Series Database &
                                                                                        Monitoring Solution.
                                                                                  spec:
                                                                                    replicas: 1
                                                                                    selector:
                                                                                      matchLabels:
                                                                                        k8s-app: victoriametrics
                                                                                        purpose: victoriametrics
                                                                                    template:
                                                                                      metadata:
                                                                                        name: victoriametrics
                                                                                        creationTimestamp: null
                                                                                        labels:
                                                                                          k8s-app: victoriametrics
                                                                                          purpose: victoriametrics
                                                                                        annotations:
                                                                                          name: >-
                                                                                            VictoriaMetrics - The High Performance Open Source Time Series
                                                                                            Database & Monitoring Solution.
                                                                                      spec:
                                                                                        containers:
                                                                                          - name: victoriametrics
                                                                                            image: 714154805721.dkr.ecr.us-east-1.amazonaws.com/victoriametrics:v1.68.0
                                                                                            ports:
                                                                                              - containerPort: 8428
                                                                                                protocol: TCP
                                                                                              - containerPort: 2003
                                                                                                protocol: TCP
                                                                                              - containerPort: 2003
                                                                                                protocol: UDP
                                                                                            volumeMounts:
                                                                                              - mountPath: /data
                                                                                                name: victoriametrics-data
                                                                                              - mountPath: /var/log
                                                                                                name: varlog
                                                                                            env:
                                                                                              - name: Name
                                                                                                value: victoriametrics
                                                                                            resources:
                                                                                              limits:
                                                                                                cpu: '1'
                                                                                                memory: 1Gi
                                                                                              requests:
                                                                                                cpu: 500m
                                                                                                memory: 1Gi
                                                                                            terminationMessagePath: /dev/termination-log
                                                                                            terminationMessagePolicy: File
                                                                                            imagePullPolicy: IfNotPresent
                                                                                        volumes:
                                                                                        - name: victoriametrics-data
                                                                                          emptyDir: {}
                                                                                        - name: varlog
                                                                                          emptyDir: {}    
                                                                                        restartPolicy: Always
                                                                                        terminationGracePeriodSeconds: 30
                                                                                        dnsPolicy: ClusterFirst
                                                                                        automountServiceAccountToken: true
                                                                                        shareProcessNamespace: false
                                                                                        securityContext: {}
                                                                                        schedulerName: default-scheduler
                                                                                        tolerations:
                                                                                          - key: k8s-app
                                                                                            operator: Equal
                                                                                            value: victoriametrics
                                                                                            effect: NoSchedule
                                                                                        enableServiceLinks: true
                                                                                    strategy:
                                                                                      type: RollingUpdate
                                                                                      rollingUpdate:
                                                                                        maxUnavailable: 25%
                                                                                        maxSurge: 25%
                                                                                    minReadySeconds: 15
                                                                                    revisionHistoryLimit: 10
                                                                                    progressDeadlineSeconds: 300
                                                                                  

                                                                                  ANSWER

                                                                                  Answered 2021-Nov-10 at 02:26

                                                                                  You need to use the persistent volume claim that you have created instead of emptyDir in your deployment:

                                                                                  kind: Deployment
                                                                                  apiVersion: apps/v1
                                                                                  metadata:
                                                                                    name: victoriametrics
                                                                                  ...
                                                                                    volumes:
                                                                                    - name: victoriametrics-data
                                                                                        persistentVolumeClaim:
                                                                                          claimName: 
                                                                                  

                                                                                  Source https://stackoverflow.com/questions/69902046

                                                                                  QUESTION

                                                                                  InfluxDB not starting: 8086 bind address already in use
                                                                                  Asked 2021-Oct-07 at 15:50

                                                                                  I have an InfluxDB Version 1.8.9, but I can't start it. In this example I'm logged in as a root.

                                                                                  netstat -lptn
                                                                                  

                                                                                  gives me a range of services, none of them seem to listen to 8086. (there are other services running like grafana or MySQL, which seem to work fine)

                                                                                  To further confirm nothing is on 8086,I listened to that related Issue run: open server: open service: listen tcp :8086: bind: address already in use on starting influxdb and run

                                                                                  netstat -a | grep 8086
                                                                                  

                                                                                  which results in no results.

                                                                                  My config file on /etc/influxdb/influxdb.conf looks like this:

                                                                                  reporting-disabled = false
                                                                                  bind-address = "127.0.0.1:8086"
                                                                                  
                                                                                  [meta]
                                                                                    #dir = "/root/.influxdb/meta"
                                                                                    dir = "/var/lib/influxdb/meta"
                                                                                    retention-autocreate = true
                                                                                    logging-enabled = true
                                                                                  
                                                                                  [data]
                                                                                    dir = "/var/lib/influxdb/data"
                                                                                    index-version = "inmem"
                                                                                    wal-dir = "/var/lib/influxdb/wal"
                                                                                    wal-fsync-delay = "0s"
                                                                                    validate-keys = false
                                                                                    strict-error-handling = false
                                                                                    query-log-enabled = true
                                                                                    cache-max-memory-size = 1073741824
                                                                                    cache-snapshot-memory-size = 26214400
                                                                                    cache-snapshot-write-cold-duration = "10m0s"
                                                                                    compact-full-write-cold-duration = "4h0m0s"
                                                                                    compact-throughput = 50331648
                                                                                    compact-throughput-burst = 50331648
                                                                                    max-series-per-database = 1000000
                                                                                    max-values-per-tag = 100000
                                                                                    max-concurrent-compactions = 0
                                                                                    max-index-log-file-size = 1048576
                                                                                    series-id-set-cache-size = 100
                                                                                    series-file-max-concurrent-snapshot-compactions = 0
                                                                                    trace-logging-enabled = false
                                                                                    tsm-use-madv-willneed = false
                                                                                    ...
                                                                                    [http]
                                                                                    enabled = true
                                                                                    bind-address = ":8086"
                                                                                    auth-enabled = false
                                                                                    log-enabled = true
                                                                                    suppress-write-log = false
                                                                                    write-tracing = false
                                                                                    flux-enabled = false
                                                                                    flux-log-enabled = false
                                                                                    pprof-enabled = true
                                                                                    pprof-auth-enabled = false
                                                                                    debug-pprof-enabled = false
                                                                                    ping-auth-enabled = false
                                                                                    prom-read-auth-enabled = false
                                                                                    https-enabled = false
                                                                                    https-certificate = "/etc/ssl/influxdb.pem"
                                                                                    https-private-key = ""
                                                                                    max-row-limit = 0
                                                                                    max-connection-limit = 0
                                                                                    shared-secret = ""
                                                                                    realm = "InfluxDB"
                                                                                    unix-socket-enabled = false
                                                                                    unix-socket-permissions = "0777"
                                                                                    bind-socket = "/var/run/influxdb.sock"
                                                                                    max-body-size = 25000000
                                                                                    access-log-path = ""
                                                                                    max-concurrent-write-limit = 0
                                                                                    max-enqueued-write-limit = 0
                                                                                    enqueued-write-timeout = 30000000000
                                                                                    ...
                                                                                  

                                                                                  So i tried to start my database:

                                                                                  service influxdb start
                                                                                  

                                                                                  Which gives me

                                                                                  ob for influxdb.service failed because a timeout was exceeded. See "systemctl status influxdb.service" and "journalctl -xe" for details.

                                                                                  result of systemctl status influxdb.service

                                                                                  ● influxdb.service - InfluxDB is an open-source, distributed, time series database
                                                                                     Loaded: loaded (/lib/systemd/system/influxdb.service; enabled; vendor preset: enabled)
                                                                                     Active: activating (start) since Tue 2021-09-21 18:37:12 CEST; 1min 7s ago
                                                                                       Docs: https://docs.influxdata.com/influxdb/
                                                                                   Main PID: 32016 (code=exited, status=1/FAILURE); Control PID: 5874 (influxd-systemd)
                                                                                      Tasks: 2 (limit: 4915)
                                                                                     CGroup: /system.slice/influxdb.service
                                                                                             ├─5874 /bin/bash -e /usr/lib/influxdb/scripts/influxd-systemd-start.sh
                                                                                             └─5965 sleep 10
                                                                                  
                                                                                  Sep 21 18:37:23 s22227708 influxd-systemd-start.sh[5874]: ts=2021-09-21T16:37:23.515897Z lvl=info msg="Registered diagnostics client" log_id=0WjJLI7l000 service=monitor name=runtime
                                                                                  Sep 21 18:37:23 s22227708 influxd-systemd-start.sh[5874]: ts=2021-09-21T16:37:23.515907Z lvl=info msg="Registered diagnostics client" log_id=0WjJLI7l000 service=monitor name=network
                                                                                  Sep 21 18:37:23 s22227708 influxd-systemd-start.sh[5874]: ts=2021-09-21T16:37:23.515923Z lvl=info msg="Registered diagnostics client" log_id=0WjJLI7l000 service=monitor name=system
                                                                                  Sep 21 18:37:23 s22227708 influxd-systemd-start.sh[5874]: ts=2021-09-21T16:37:23.515977Z lvl=info msg="Starting precreation service" log_id=0WjJLI7l000 service=shard-precreation check_interval=10m advanc
                                                                                  Sep 21 18:37:23 s22227708 influxd-systemd-start.sh[5874]: ts=2021-09-21T16:37:23.515995Z lvl=info msg="Starting snapshot service" log_id=0WjJLI7l000 service=snapshot
                                                                                  Sep 21 18:37:23 s22227708 influxd-systemd-start.sh[5874]: ts=2021-09-21T16:37:23.516015Z lvl=info msg="Starting continuous query service" log_id=0WjJLI7l000 service=continuous_querier
                                                                                  Sep 21 18:37:23 s22227708 influxd-systemd-start.sh[5874]: ts=2021-09-21T16:37:23.516011Z lvl=info msg="Storing statistics" log_id=0WjJLI7l000 service=monitor db_instance=_internal db_rp=monitor interval=
                                                                                  Sep 21 18:37:23 s22227708 influxd-systemd-start.sh[5874]: ts=2021-09-21T16:37:23.516037Z lvl=info msg="Starting HTTP service" log_id=0WjJLI7l000 service=httpd authentication=false
                                                                                  Sep 21 18:37:23 s22227708 influxd-systemd-start.sh[5874]: ts=2021-09-21T16:37:23.516052Z lvl=info msg="opened HTTP access log" log_id=0WjJLI7l000 service=httpd path=stderr
                                                                                  Sep 21 18:37:23 s22227708 influxd-systemd-start.sh[5874]: run: open server: open service: listen tcp :8086: bind: address already in use
                                                                                  

                                                                                  I can't really understand where I did something wrong, since I configured :8086 in the config file. Can you help me?

                                                                                  ANSWER

                                                                                  Answered 2021-Sep-21 at 17:57

                                                                                  It appears to be a typo in the configuration file. As stated in the documentation, the configuration file should hold http-bind-address instead of bind-address. As well as a locked port by the first configuration.

                                                                                  The first few lines of the file /etc/influxdb/influxdb.conf should look like so:

                                                                                  reporting-disabled = false
                                                                                  http-bind-address = "127.0.0.1:8086"
                                                                                  

                                                                                  A suggested approach would be to:

                                                                                  1. bind-address to http-bind-address
                                                                                  2. Changing the port from default 8086 to a known free port
                                                                                  3. (Optional) Back to the default port.

                                                                                  Source https://stackoverflow.com/questions/69272620

                                                                                  QUESTION

                                                                                  Writing the data to the timeseries database over unstable network
                                                                                  Asked 2021-Sep-14 at 22:08

                                                                                  I'm trying to find a time series database for the following scenario:

                                                                                  1. Some sensor on raspberry pi provides the realtime data.
                                                                                  2. Some application takes the data and pushes to the time series database.
                                                                                  3. If network is off (GSM modem ran out of money or rain or something else), store data locally.
                                                                                  4. Once network is available the data should be synchronised to the time series database in the cloud. So no missing data and no duplicates.
                                                                                  5. (Optionally) query database from Grafana

                                                                                  I'm looking for time series database that can handle 3. and 4. for me. Is there any?

                                                                                  I can start Prometheus in federated mode (Can I?) and keep one node on raspberry pi for initial ingestion and another node in the cloud for collecting the data. But that setup would instantly consume 64mb+ of memory for Prometheus node.

                                                                                  ANSWER

                                                                                  Answered 2021-Sep-14 at 22:08

                                                                                  Take a look at vmagent. It can be installed at every device where metrics from local sensors must be collected (e.g. at the edge), and collect all these metrics via various popular data ingestion protocols. Then it can push the collected metrics to a centralized time series database such as VictoriaMetrics. Vmagent buffers the collected metrics on the local storage when the connection to a centralized database is unavailable, and pushes the buffered data to the database as soon as the connection is recovered. Vmagent works on Rasberry PI and on any device with ARM, ARM64 or AMD64 architecture.

                                                                                  See use cases for vmagent for more details.

                                                                                  Source https://stackoverflow.com/questions/69180563

                                                                                  QUESTION

                                                                                  Recommended approach to store multi-dimensional data (e.g. spectra) in InfluxDB
                                                                                  Asked 2021-Sep-05 at 11:04

                                                                                  I am trying to incorporate the time series database with the laboratory real time monitoring equipment. For scalar data such as temperature the line protocol works well:

                                                                                  temperature,site=reactor temperature=20.0 1556892576842902000
                                                                                  

                                                                                  For 1D (e.g., IR Spectrum) or higher dimensional data, I came up two approaches to write data.

                                                                                  1. Write each element of the spectrum as field set as shown below. This way I can query individual frequency and perform analysis or visualization using the existing software. However, each record will easily contain thousands of field sets due to the high resolution of the spectrometer. My concern is whether the line protocol is too chunky and the storage can get inefficient or not.
                                                                                  ir_spectrum,site=reactor w1=10.0,w2=11.2,w3=11.3,......,w4000=2665.2 1556892576842902000
                                                                                  
                                                                                  1. Store the vector as a serialized string (e.g., JSON). This way I may need some plugins to adapt the data to the visualization tools such as Grafana. But the protocol will look cleaner. I am not sure whether the storage layout is better than the first approach or not.
                                                                                  ir_spectrum,site=reactor data="[10.0, 11.2, 11.3, ......, 2665.2]" 1556892576842902000
                                                                                  

                                                                                  I wonder whether there is any recommended way to store the high dimensional data? Thanks!

                                                                                  ANSWER

                                                                                  Answered 2021-Sep-05 at 11:04

                                                                                  The first approach is better from the performance and disk space usage PoV. InfluxDB stores each field in a separate column. If a column contains similar numeric values, then it may be compressed better compared to the column with JSON strings. This also improves query speed when selecting only a subset of fields or filtering on a subset of fields.

                                                                                  P.S. InfluxDB may need high amounts of RAM for big number of fields and big number of tag combinations (aka high cardinality). In this case there are alternative solutions, which support InfluxDB line protocol and require lower amounts of RAM for high cardinality time series. See, for example, VictoriaMetrics.

                                                                                  Source https://stackoverflow.com/questions/69008057

                                                                                  QUESTION

                                                                                  What to report in a time serie database when the measure failed?
                                                                                  Asked 2021-Jun-08 at 13:53

                                                                                  I use a time series database to report some network metrics, such as the download time or DNS lookup time for some endpoints. However, sometimes the measure fails like if the endpoint is down, or if there is a network issue. In theses cases, what should be done according to the best practices? Should I report an impossible value, like -1, or just not write anything at all in the database?

                                                                                  The problem I see when not writing anything, is that I cannot know if my test is not running anymore, or if it is a problem with the endpoint/network.

                                                                                  ANSWER

                                                                                  Answered 2021-Jun-08 at 13:53

                                                                                  The best practice is to capture the failures in their own time series for separate analysis.

                                                                                  Failures or bad readings will skew the series, so they should be filtered out or replaced with a projected value for 'normal' events. The beauty of a time series is that one measure (time) is globally common, so it is easy to project between two known points when one is missing.

                                                                                  The failure information is also important, as it is an early indicator to issues or outages on your target. You can record the network error and other diagnostic information to find trends and ensure it is the client and not your server having the issue. Further, there can be several instances deployed to monitor the same target so that they cancel each other's noise.

                                                                                  You can also monitor a known endpoint like google's 204 page to ensure network connectivity. If all the monitors report an error connecting to your site but not to the known endpoint, your server is indeed down.

                                                                                  Source https://stackoverflow.com/questions/67701340

                                                                                  QUESTION

                                                                                  R ggplot customize month labels in time series
                                                                                  Asked 2021-May-18 at 21:58

                                                                                  I have a database that is being used to create a time series. The date column in the time series database is formatted as a POSIXct date format.

                                                                                  Database
                                                                                  data <- structure(list(TimeStep = c("1", "1", "1", "1", "10", "10", "10", 
                                                                                  "10", "11", "11", "11", "11", "12", "12", "12", "12", "2", "2", 
                                                                                  "2", "2", "3", "3", "3", "3", "4", "4", "4", "4", "5", "5", "5", 
                                                                                  "5", "6", "6", "6", "6", "7", "7", "7", "7", "8", "8", "8", "8", 
                                                                                  "9", "9", "9", "9"), Shelter = structure(c(1L, 1L, 2L, 2L, 1L, 
                                                                                  1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 
                                                                                  1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 
                                                                                  1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L), .Label = c("Low", 
                                                                                  "High"), class = c("ordered", "factor")), Site_long = structure(c(2L, 
                                                                                  1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
                                                                                  1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
                                                                                  1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L), .Label = c("Hanauma Bay", 
                                                                                  "Waikiki"), class = c("ordered", "factor")), mean = c(0, 0.00015328484505956, 
                                                                                  7.04072939791738e-06, 0.000682330358210582, 2.66666666666667e-06, 
                                                                                  6.00605206484062e-07, 0, 0.000386079421618121, 0, 0.000107757674499178, 
                                                                                  3.38820905388829e-05, 0.000309370042627687, 2.66666666666667e-06, 
                                                                                  0.000258419039004161, 3.40590905388829e-05, 0.000600512507641285, 
                                                                                  0, 0.000252408374787308, 7.40825319693649e-05, 0.000656756473864922, 
                                                                                  8.69852169224144e-05, 7.75576156462366e-05, 8.33333333333333e-08, 
                                                                                  0.000665098673793383, 0, 2.04566422429192e-05, 5.31031263835315e-05, 
                                                                                  0.000647037987463065, 0, 0.000198961690069793, 0, 0.000423723440028656, 
                                                                                  8.16572939791738e-06, 0.00012785593538717, 2.34677162777718e-08, 
                                                                                  0.000224655093601419, 3.33333333333333e-07, 7.42595319693649e-05, 
                                                                                  0.00039116506393873, 2.13882406395912e-05, 2.66666666666667e-06, 
                                                                                  0.000107081818106607, 4.16666666666667e-08, 3.77742205841092e-05, 
                                                                                  1.88237465916921e-05, 0.00010916313850296, 0, 7.6903907646831e-05
                                                                                  ), sd = c(0, 0.00015966459922941, 9.95709500353371e-06, 0.000482307005382674, 
                                                                                  3.77123616632825e-06, 8.0011788810835e-07, 0, 0.000440508169853482, 
                                                                                  0, 0.000152392364726523, 4.79165119616413e-05, 0.000309520641549238, 
                                                                                  3.77123616632825e-06, 0.000184777835884834, 4.81668277621813e-05, 
                                                                                  0.000477723602613157, 0, 0.000179807492264746, 0.000104768521446014, 
                                                                                  0.000482423300574339, 0.000123015673497644, 0.000105040104106768, 
                                                                                  1.17851130197758e-07, 0.000498359190138349, 0, 2.55445716349544e-05, 
                                                                                  7.50991615360028e-05, 0.000105303327368202, 0, 0.000185382956021377, 
                                                                                  0, 0.000316153398426712, 1.15480852612034e-05, 9.37823230585177e-05, 
                                                                                  3.31883626379487e-08, 0.000297053654293301, 4.71404520791032e-07, 
                                                                                  0.000104643588086737, 0.000406298601551981, 1.51238560888153e-05, 
                                                                                  3.77123616632825e-06, 0.000145843975969992, 5.8925565098879e-08, 
                                                                                  2.23804174757353e-05, 2.66207977246453e-05, 0.000145131278731618, 
                                                                                  0, 5.83391012319395e-05), lower = c(0, 6.11024457144067e-05, 
                                                                                  1.29199791728051e-06, 0.000403870278820852, 4.89342450859397e-07, 
                                                                                  1.38656928401271e-07, 0, 0.000131751911172984, 0, 1.97739017018764e-05, 
                                                                                  6.21747945920136e-06, 0.000130668216909489, 4.89342450859397e-07, 
                                                                                  0.000151737505715775, 6.24995956437715e-06, 0.000324698657074341, 
                                                                                  0, 0.000148596470725945, 1.35943979099095e-05, 0.000378229251414979, 
                                                                                  1.59620847140065e-05, 1.69126832644878e-05, 1.52919515893562e-08, 
                                                                                  0.000377370861213884, 0, 5.70847693314461e-06, 9.74460525480512e-06, 
                                                                                  0.000586241083060471, 0, 9.19307905076825e-05, 0, 0.000241192190341779, 
                                                                                  1.49843926373682e-06, 7.37106859241061e-05, 4.30640617478918e-09, 
                                                                                  5.31510863314202e-05, 6.11678063574246e-08, 1.38435282185187e-05, 
                                                                                  0.000156588456961325, 1.26564782555285e-05, 4.89342450859397e-07, 
                                                                                  2.2878759320647e-05, 7.64597579467808e-09, 2.48528805299173e-05, 
                                                                                  3.45422185932556e-06, 2.53715556594257e-05, 0, 4.32218118462899e-05
                                                                                  ), upper = c(0, 0.000245467244404714, 1.27894608785542e-05, 0.000960790437600311, 
                                                                                  4.84399088247394e-06, 1.06255348456685e-06, 0, 0.000640406932063258, 
                                                                                  0, 0.00019574144729648, 6.15467016185644e-05, 0.000488071868345885, 
                                                                                  4.84399088247394e-06, 0.000365100572292546, 6.18682215133886e-05, 
                                                                                  0.000876326358208229, 0, 0.00035622027884867, 0.00013457066602882, 
                                                                                  0.000935283696314864, 0.000158008349130822, 0.000138202548027985, 
                                                                                  1.51374715077311e-07, 0.000952826486372883, 0, 3.52048075526939e-05, 
                                                                                  9.64616475122579e-05, 0.00070783489186566, 0, 0.000305992589631903, 
                                                                                  0, 0.000606254689715534, 1.48330195320979e-05, 0.000182001184850233, 
                                                                                  4.26290263807544e-08, 0.000396159100871418, 6.05498860309242e-07, 
                                                                                  0.000134675535720211, 0.000625741670916134, 3.01200030236539e-05, 
                                                                                  4.84399088247394e-06, 0.000191284876892567, 7.56873575386553e-08, 
                                                                                  5.06955606383012e-05, 3.41932713240586e-05, 0.000192954721346495, 
                                                                                  0, 0.000110586003447372), Date_new = structure(c(17311, 17323, 
                                                                                  17311, 17323, 18154, 18149, 18154, 18149, 18244, 18240, 18244, 
                                                                                  18240, 18309, 18338, 18310.6666666667, 18338, 17419, 17414, 17419, 
                                                                                  17414, 17503, 17498, 17503, 17498, 17596.3333333333, 17561, 17605.3333333333, 
                                                                                  17561, 17671, 17666, 17671, 17666, 17775, 17771, 17775, 17771, 
                                                                                  17873, 17869, 17873, 17869, 17977, 17974, 17977, 17974, 18050, 
                                                                                  18051, 18050, 18051), class = "Date")), row.names = c(NA, -48L
                                                                                  ), groups = structure(list(TimeStep = c("1", "1", "10", "10", 
                                                                                  "11", "11", "12", "12", "2", "2", "3", "3", "4", "4", "5", "5", 
                                                                                  "6", "6", "7", "7", "8", "8", "9", "9"), Shelter = structure(c(1L, 
                                                                                  2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
                                                                                  2L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c("Low", "High"), class = c("ordered", 
                                                                                  "factor")), .rows = structure(list(1:2, 3:4, 5:6, 7:8, 9:10, 
                                                                                      11:12, 13:14, 15:16, 17:18, 19:20, 21:22, 23:24, 25:26, 27:28, 
                                                                                      29:30, 31:32, 33:34, 35:36, 37:38, 39:40, 41:42, 43:44, 45:46, 
                                                                                      47:48), ptype = integer(0), class = c("vctrs_list_of", "vctrs_vctr", 
                                                                                  "list"))), row.names = c(NA, -24L), class = c("tbl_df", "tbl", 
                                                                                  "data.frame"), .drop = TRUE), class = c("grouped_df", "tbl_df", 
                                                                                  "tbl", "data.frame"))
                                                                                  

                                                                                  When I plot these data using month only labels at three-month increments, I get labels at "Jul", "Oct", "Jan", and "Apr", when I actually want the month labels to be "Jun", "Sep", "Dec", and "Mar" instead. I have tried specifying the labels as a character vector of these month abbreviations to no avail.

                                                                                  Plot Code
                                                                                  urchin_time_series_plot <- ggplot(data = data, aes(x = Date_new, y = mean, fill = Shelter, shape = Site_long)) + 
                                                                                    geom_point(aes(size = 3)) +
                                                                                    geom_line(aes(linetype = Shelter)) +
                                                                                    scale_linetype_manual(values=c("dashed", "solid")) +
                                                                                    scale_shape_manual(values = c(21, 24)) +
                                                                                    scale_fill_manual(values = c(NA, "black"), guide = guide_legend(override.aes = list(shape = 21))) +
                                                                                    scale_y_continuous(labels = scientific) +
                                                                                    guides(size = FALSE) +
                                                                                    geom_errorbar(aes(ymin = lower, ymax = upper), width = 0) + 
                                                                                    scale_x_date(date_breaks = "3 months", date_labels = "%b") +
                                                                                    labs(x = "Date", y = "Urchin biomass (Kg)") +
                                                                                    theme_bw() + theme(text = element_text(size = 14), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.title.x = element_blank(), axis.text = element_text(size = 16), legend.text = element_text(size = rel(1.5)), axis.text.y = element_text(angle = 90, hjust = .9), legend.title = element_text(size = rel(1.5)), legend.position = "none", axis.title.y = element_text(size = 18), axis.title = element_text(size = 16)) 
                                                                                  
                                                                                  Output

                                                                                  Any suggestions to shift the months on the x-axis labels back one month to "Jun", "Sep", "Dec", and "Mar" would be greatly appreciated.

                                                                                  ANSWER

                                                                                  Answered 2021-May-18 at 21:58

                                                                                  The solution I found is to expand the date range using the expand_limits() function in ggplot2 so that some days in May are included. By padding the range, I get the correct output

                                                                                  urchin_time_series_plot <- ggplot(data = plot_data_urchin, aes(x = Date_new, y = mean, fill = Shelter, shape = Site_long)) + 
                                                                                    geom_point(aes(size = 3)) +
                                                                                    geom_line(aes(linetype = Shelter)) +
                                                                                    scale_linetype_manual(values=c("dashed", "solid")) +
                                                                                    scale_shape_manual(values = c(21, 24)) +
                                                                                    scale_fill_manual(values = c(NA, "black"), guide = guide_legend(override.aes = list(shape = 21))) +
                                                                                    scale_y_continuous(labels = scientific) +
                                                                                    guides(size = FALSE) +
                                                                                    geom_errorbar(aes(ymin = lower, ymax = upper), width = 0) + 
                                                                                    scale_x_date(date_breaks = "3 months", date_labels = "%b") +
                                                                                    expand_limits(x = as.Date(c("2017-05-15", "2020-04-01"))) +
                                                                                    labs(x = "Date", y = "Urchin biomass (Kg)") +
                                                                                    theme_bw() + theme(text = element_text(size = 14), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.title.x = element_blank(), axis.text = element_text(size = 16), legend.text = element_text(size = rel(1.5)), axis.text.y = element_text(angle = 90, hjust = .9), legend.title = element_text(size = rel(1.5)), legend.position = "none", axis.title.y = element_text(size = 18), axis.title = element_text(size = 16)) 
                                                                                  

                                                                                  Source https://stackoverflow.com/questions/67592610

                                                                                  QUESTION

                                                                                  How Can I Generate A Visualisation with Multiple Data Series In Splunk
                                                                                  Asked 2021-Apr-29 at 13:11

                                                                                  I have been experimenting with Splunk, trying to emulate some basic functionality from the OSISoft PI Time Series database.

                                                                                  I have two data points that I wish to display trends for over time in order to compare fluctuations between them, specifically power network MW analogue tags.

                                                                                  In PI this is very easy to do, however I am having difficulty figuring out how to do it in Splunk.

                                                                                  How do I achieve this given the field values "SubstationA_T1_MW", & "SubstationA_T2_MW" in the field Tag?

                                                                                  The fields involved are TimeStamp, Tag, Value, and Status

                                                                                  Edit:

                                                                                  Sample Input and Output listed below:

                                                                                  ANSWER

                                                                                  Answered 2021-Apr-29 at 12:41

                                                                                  I suspect you're going to be most interested in timechart for this

                                                                                  Something along the following lines may get you towards what you're looking for:

                                                                                  index=ndx sourcetype=srctp Value=* TimeStamp=* %NStatus=* (Tag=SubstationA_T1_MW OR Tag=SubstationA_T2_MW) earliest=-2h
                                                                                  | eval _time=strptime(TimeStamp,"%m/%d/%Y %H:%M:%S.%N")
                                                                                  | timechart span=15m max(Value) as Value by Tag
                                                                                  

                                                                                  timechart relies on the internal, hidden _time field (which is in Unix epoch time) - so if _time doesn't match TimeStamp, you need the eval statement I added to convert from your TimeStamp to Unix epoch time in _time (which I've assumed is in mm/dd/yyyy format).

                                                                                  Also, go take the free, self-paced Splunk Fundamentals 1 class

                                                                                  Source https://stackoverflow.com/questions/67304621

                                                                                  QUESTION

                                                                                  How can I deploy QuestDB on GCP?
                                                                                  Asked 2021-Apr-08 at 09:38

                                                                                  I would like to deploy the time series database QuestDB on GCP, but I do not see any instructions on the documentation. Could I get some steps?

                                                                                  ANSWER

                                                                                  Answered 2021-Apr-08 at 09:38

                                                                                  This can be done in a few shorts steps on Compute Engine. When creating a new instance, choose the region and instance type, then:

                                                                                  • In the "Container" section, enable "Deploy a container image to this VM instance"
                                                                                  • type questdb/questdb:latest for the "Container image"

                                                                                  This will pull the latest QuestDB docker image and run it on your instance when launching. The rest of the setup steps are setting firewall rules to allow networking on the ports you require:

                                                                                  • port 9000 - web console & REST API
                                                                                  • port 8812 - PostgreSQL wire protocol

                                                                                  Source of this info is an ETL tutorial by Gabor Boros which deploys QuestDB to GCP and uses Cloud Functions for loading and processing data from a storage bucket.

                                                                                  Source https://stackoverflow.com/questions/66805126

                                                                                  QUESTION

                                                                                  Group By day for custom time interval
                                                                                  Asked 2021-Mar-23 at 09:47

                                                                                  I'm very new to SQL and time series database. I'm using crate database. I want to aggregate the data by day. But the I want to start each day start time is 9 AM not 12AM..

                                                                                  Time interval is 9 AM to 11.59 PM.

                                                                                  Unix time stamp is used to store the data. following is my sample database.

                                                                                  |sensorid | reading    | timestamp|
                                                                                  ====================================
                                                                                  |1        | 1616457600 | 10       |
                                                                                  |1        | 1616461200 | 100      | 
                                                                                  |2        | 1616493600 | 1        |
                                                                                  |2        | 1616493601 | 10        |
                                                                                  

                                                                                  Currently i grouped using following command. But it gives the start time as 12 AM.

                                                                                  select date_trunc('day', v.timestamp) as day,sum(reading)
                                                                                  from sensor1  v(timestamp)
                                                                                  group by  (DAY)
                                                                                  

                                                                                  From the above table. i want sum of 1616493600 and 1616493601 data (11 is result). because 1616457600 and 1616461200 are less than 9 am data.

                                                                                  ANSWER

                                                                                  Answered 2021-Mar-23 at 09:47

                                                                                  You want to add nine hours to midnight:

                                                                                  date_trunc('day', v.timestamp) + interval '9' hour
                                                                                  

                                                                                  Edit: If you want to exclude hours before 9:00 from the data you add up, you must add a WHERE clause:

                                                                                  where extract(hour from v.timestamp) >= 9
                                                                                  

                                                                                  Here is a complete query with all relevant data:

                                                                                  select
                                                                                    date_trunc('day', v.timestamp) as day,
                                                                                    date_trunc('day', v.timestamp) + interval '9' hour as day_start,
                                                                                    min(v.timestamp) as first_data,
                                                                                    max(v.timestamp) as last_data,
                                                                                    sum(reading) as total_reading
                                                                                  from sensor1  v(timestamp)
                                                                                  where extract(hour from v.timestamp) >= 9
                                                                                  group by day
                                                                                  order by day;
                                                                                  

                                                                                  Source https://stackoverflow.com/questions/66759638

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                                                                                  Install Python-3-basics-series

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