# Google Monitoring API . projects . collectdTimeSeries

## Instance Methods

Creates a new time series with the given data points. This method is only for use in `collectd`-related code, including the Google Monitoring Agent. See [google.monitoring.v3.MetricService.CreateTimeSeries] instead.

### Method Details

`create(name, body, x__xgafv=None)`

    Creates a new time series with the given data points. This method is only for use in `collectd`-related code, including the Google Monitoring Agent. See [google.monitoring.v3.MetricService.CreateTimeSeries] instead.

    Args:
      name: string, The project in which to create the time series. The format is `"projects/PROJECT_ID_OR_NUMBER"`. (required)
      body: object, The request body. (required)
        The object takes the form of:

    { # The `CreateCollectdTimeSeries` request.
        "collectdPayloads": [ # The `collectd` payloads representing the time series data. You must not include more than a single point for each time series, so no two payloads can have the same values for all of the fields `plugin`, `plugin_instance`, `type`, and `type_instance`.
          { # A collection of data points sent from a `collectd`-based plugin. See the `collectd` documentation for more information.
            "plugin": "A String", # The name of the plugin. Example: `"disk"`.
            "typeInstance": "A String", # The measurement type instance. Example: `"used"`.
            "values": [ # The measured values during this time interval. Each value must have a different `dataSourceName`.
              { # A single data point from a `collectd`-based plugin.
                "dataSourceType": "A String", # The type of measurement.
                "dataSourceName": "A String", # The data source for the `collectd` value. For example there are two data sources for network measurements: `"rx"` and `"tx"`.
                "value": { # A single strongly-typed value. # The measurement value.
                  "distributionValue": { # Distribution contains summary statistics for a population of values and, optionally, a histogram representing the distribution of those values across a specified set of histogram buckets. The summary statistics are the count, mean, sum of the squared deviation from the mean, the minimum, and the maximum of the set of population of values. The histogram is based on a sequence of buckets and gives a count of values that fall into each bucket. The boundaries of the buckets are given either explicitly or by specifying parameters for a method of computing them (buckets of fixed width or buckets of exponentially increasing width). Although it is not forbidden, it is generally a bad idea to include non-finite values (infinities or NaNs) in the population of values, as this will render the `mean` and `sum_of_squared_deviation` fields meaningless. # A distribution value.
                    "count": "A String", # The number of values in the population. Must be non-negative.
                    "bucketCounts": [ # If `bucket_options` is given, then the sum of the values in `bucket_counts` must equal the value in `count`. If `bucket_options` is not given, no `bucket_counts` fields may be given. Bucket counts are given in order under the numbering scheme described above (the underflow bucket has number 0; the finite buckets, if any, have numbers 1 through N-2; the overflow bucket has number N-1). The size of `bucket_counts` must be no greater than N as defined in `bucket_options`. Any suffix of trailing zero bucket_count fields may be omitted.
                      "A String",
                    ],
                    "bucketOptions": { # A Distribution may optionally contain a histogram of the values in the population. The histogram is given in `bucket_counts` as counts of values that fall into one of a sequence of non-overlapping buckets. The sequence of buckets is described by `bucket_options`. A bucket specifies an inclusive lower bound and exclusive upper bound for the values that are counted for that bucket. The upper bound of a bucket is strictly greater than the lower bound. The sequence of N buckets for a Distribution consists of an underflow bucket (number 0), zero or more finite buckets (number 1 through N - 2) and an overflow bucket (number N - 1). The buckets are contiguous: the lower bound of bucket i (i > 0) is the same as the upper bound of bucket i - 1. The buckets span the whole range of finite values: lower bound of the underflow bucket is -infinity and the upper bound of the overflow bucket is +infinity. The finite buckets are so-called because both bounds are finite. `BucketOptions` describes bucket boundaries in one of three ways. Two describe the boundaries by giving parameters for a formula to generate boundaries and one gives the bucket boundaries explicitly. If `bucket_boundaries` is not given, then no `bucket_counts` may be given. # Defines the histogram bucket boundaries.
                      "exponentialBuckets": { # Specify a sequence of buckets that have a width that is proportional to the value of the lower bound. Each bucket represents a constant relative uncertainty on a specific value in the bucket. Defines `num_finite_buckets + 2` (= N) buckets with these boundaries for bucket i: Upper bound (0 <= i < N-1): scale * (growth_factor ^ i). Lower bound (1 <= i < N): scale * (growth_factor ^ (i - 1)). # The exponential buckets.
                        "scale": 3.14, # Must be greater than 0
                        "growthFactor": 3.14, # Must be greater than 1
                        "numFiniteBuckets": 42, # must be greater than 0
                      },
                      "linearBuckets": { # Specify a sequence of buckets that all have the same width (except overflow and underflow). Each bucket represents a constant absolute uncertainty on the specific value in the bucket. Defines `num_finite_buckets + 2` (= N) buckets with these boundaries for bucket `i`: Upper bound (0 <= i < N-1): offset + (width * i). Lower bound (1 <= i < N): offset + (width * (i - 1)). # The linear bucket.
                        "width": 3.14, # Must be greater than 0.
                        "numFiniteBuckets": 42, # Must be greater than 0.
                        "offset": 3.14, # Lower bound of the first bucket.
                      },
                      "explicitBuckets": { # A set of buckets with arbitrary widths. Defines `size(bounds) + 1` (= N) buckets with these boundaries for bucket i: Upper bound (0 <= i < N-1): bounds[i] Lower bound (1 <= i < N); bounds[i - 1] There must be at least one element in `bounds`. If `bounds` has only one element, there are no finite buckets, and that single element is the common boundary of the overflow and underflow buckets. # The explicit buckets.
                        "bounds": [ # The values must be monotonically increasing.
                          3.14,
                        ],
                      },
                    },
                    "range": { # The range of the population values. # If specified, contains the range of the population values. The field must not be present if the `count` is zero.
                      "max": 3.14, # The maximum of the population values.
                      "min": 3.14, # The minimum of the population values.
                    },
                    "sumOfSquaredDeviation": 3.14, # The sum of squared deviations from the mean of the values in the population. For values x_i this is: Sum[i=1..n]((x_i - mean)^2) Knuth, "The Art of Computer Programming", Vol. 2, page 323, 3rd edition describes Welford's method for accumulating this sum in one pass. If `count` is zero then this field must be zero.
                    "mean": 3.14, # The arithmetic mean of the values in the population. If `count` is zero then this field must be zero.
                  },
                  "stringValue": "A String", # A variable-length string value.
                  "boolValue": True or False, # A Boolean value: `true` or `false`.
                  "int64Value": "A String", # A 64-bit integer. Its range is approximately ±9.2x1018.
                  "doubleValue": 3.14, # A 64-bit double-precision floating-point number. Its magnitude is approximately ±10±300 and it has 16 significant digits of precision.
                },
              },
            ],
            "startTime": "A String", # The start time of the interval.
            "endTime": "A String", # The end time of the interval.
            "type": "A String", # The measurement type. Example: `"memory"`.
            "pluginInstance": "A String", # The instance name of the plugin Example: `"hdcl"`.
            "metadata": { # The measurement metadata. Example: `"process_id" -> 12345`
              "a_key": { # A single strongly-typed value.
                "distributionValue": { # Distribution contains summary statistics for a population of values and, optionally, a histogram representing the distribution of those values across a specified set of histogram buckets. The summary statistics are the count, mean, sum of the squared deviation from the mean, the minimum, and the maximum of the set of population of values. The histogram is based on a sequence of buckets and gives a count of values that fall into each bucket. The boundaries of the buckets are given either explicitly or by specifying parameters for a method of computing them (buckets of fixed width or buckets of exponentially increasing width). Although it is not forbidden, it is generally a bad idea to include non-finite values (infinities or NaNs) in the population of values, as this will render the `mean` and `sum_of_squared_deviation` fields meaningless. # A distribution value.
                  "count": "A String", # The number of values in the population. Must be non-negative.
                  "bucketCounts": [ # If `bucket_options` is given, then the sum of the values in `bucket_counts` must equal the value in `count`. If `bucket_options` is not given, no `bucket_counts` fields may be given. Bucket counts are given in order under the numbering scheme described above (the underflow bucket has number 0; the finite buckets, if any, have numbers 1 through N-2; the overflow bucket has number N-1). The size of `bucket_counts` must be no greater than N as defined in `bucket_options`. Any suffix of trailing zero bucket_count fields may be omitted.
                    "A String",
                  ],
                  "bucketOptions": { # A Distribution may optionally contain a histogram of the values in the population. The histogram is given in `bucket_counts` as counts of values that fall into one of a sequence of non-overlapping buckets. The sequence of buckets is described by `bucket_options`. A bucket specifies an inclusive lower bound and exclusive upper bound for the values that are counted for that bucket. The upper bound of a bucket is strictly greater than the lower bound. The sequence of N buckets for a Distribution consists of an underflow bucket (number 0), zero or more finite buckets (number 1 through N - 2) and an overflow bucket (number N - 1). The buckets are contiguous: the lower bound of bucket i (i > 0) is the same as the upper bound of bucket i - 1. The buckets span the whole range of finite values: lower bound of the underflow bucket is -infinity and the upper bound of the overflow bucket is +infinity. The finite buckets are so-called because both bounds are finite. `BucketOptions` describes bucket boundaries in one of three ways. Two describe the boundaries by giving parameters for a formula to generate boundaries and one gives the bucket boundaries explicitly. If `bucket_boundaries` is not given, then no `bucket_counts` may be given. # Defines the histogram bucket boundaries.
                    "exponentialBuckets": { # Specify a sequence of buckets that have a width that is proportional to the value of the lower bound. Each bucket represents a constant relative uncertainty on a specific value in the bucket. Defines `num_finite_buckets + 2` (= N) buckets with these boundaries for bucket i: Upper bound (0 <= i < N-1): scale * (growth_factor ^ i). Lower bound (1 <= i < N): scale * (growth_factor ^ (i - 1)). # The exponential buckets.
                      "scale": 3.14, # Must be greater than 0
                      "growthFactor": 3.14, # Must be greater than 1
                      "numFiniteBuckets": 42, # must be greater than 0
                    },
                    "linearBuckets": { # Specify a sequence of buckets that all have the same width (except overflow and underflow). Each bucket represents a constant absolute uncertainty on the specific value in the bucket. Defines `num_finite_buckets + 2` (= N) buckets with these boundaries for bucket `i`: Upper bound (0 <= i < N-1): offset + (width * i). Lower bound (1 <= i < N): offset + (width * (i - 1)). # The linear bucket.
                      "width": 3.14, # Must be greater than 0.
                      "numFiniteBuckets": 42, # Must be greater than 0.
                      "offset": 3.14, # Lower bound of the first bucket.
                    },
                    "explicitBuckets": { # A set of buckets with arbitrary widths. Defines `size(bounds) + 1` (= N) buckets with these boundaries for bucket i: Upper bound (0 <= i < N-1): bounds[i] Lower bound (1 <= i < N); bounds[i - 1] There must be at least one element in `bounds`. If `bounds` has only one element, there are no finite buckets, and that single element is the common boundary of the overflow and underflow buckets. # The explicit buckets.
                      "bounds": [ # The values must be monotonically increasing.
                        3.14,
                      ],
                    },
                  },
                  "range": { # The range of the population values. # If specified, contains the range of the population values. The field must not be present if the `count` is zero.
                    "max": 3.14, # The maximum of the population values.
                    "min": 3.14, # The minimum of the population values.
                  },
                  "sumOfSquaredDeviation": 3.14, # The sum of squared deviations from the mean of the values in the population. For values x_i this is: Sum[i=1..n]((x_i - mean)^2) Knuth, "The Art of Computer Programming", Vol. 2, page 323, 3rd edition describes Welford's method for accumulating this sum in one pass. If `count` is zero then this field must be zero.
                  "mean": 3.14, # The arithmetic mean of the values in the population. If `count` is zero then this field must be zero.
                },
                "stringValue": "A String", # A variable-length string value.
                "boolValue": True or False, # A Boolean value: `true` or `false`.
                "int64Value": "A String", # A 64-bit integer. Its range is approximately ±9.2x1018.
                "doubleValue": 3.14, # A 64-bit double-precision floating-point number. Its magnitude is approximately ±10±300 and it has 16 significant digits of precision.
              },
            },
          },
        ],
        "resource": { # An object representing a resource that can be used for monitoring, logging, billing, or other purposes. Examples include virtual machine instances, databases, and storage devices such as disks. The `type` field identifies a MonitoredResourceDescriptor object that describes the resource's schema. Information in the `labels` field identifies the actual resource and its attributes according to the schema. For example, a particular Compute Engine VM instance could be represented by the following object, because the MonitoredResourceDescriptor for `"gce_instance"` has labels `"instance_id"` and `"zone"`: { "type": "gce_instance", "labels": { "instance_id": "my-instance", "zone": "us-central1-a" }} # The monitored resource associated with the time series.
          "labels": { # Required. Values for all of the labels listed in the associated monitored resource descriptor. For example, Cloud SQL databases use the labels `"database_id"` and `"zone"`.
            "a_key": "A String",
          },
          "type": "A String", # Required. The monitored resource type. This field must match the `type` field of a MonitoredResourceDescriptor object. For example, the type of a Cloud SQL database is `"cloudsql_database"`.
        },
        "collectdVersion": "A String", # The version of `collectd` that collected the data. Example: `"5.3.0-192.el6"`.
      }

      x__xgafv: string, V1 error format.

    Returns:
      An object of the form:

        { # A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for `Empty` is empty JSON object `{}`.
      }