tobz

def find_nested_object_property_schema(schema, property_name)
  @logger.debug "Starting search for nested property '#{property_name}'"
  @logger.debug "Starting schema: #{JSON.dump(get_reduced_schema(schema))}"

  # See if we're checking an object schema directly.
  if !schema['properties'].nil?
    @logger.debug "Schema is object, checking properties directly."
    return schema['properties'][property_name]
  end

tobz

FROM ghcr.io/goauthentik/server:2022.9.0
USER root
RUN sed -i -e 's# > /dev/stderr##g' /lifecycle/ak
USER authentik
ENTRYPOINT ["/lifecycle/ak"]
CMD ["server"]

tobz

---
version: '3.4'

services:
  postgresql:
    image: docker.io/library/postgres:12-alpine
    restart: unless-stopped
    network_mode: host
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -d $${POSTGRES_DB} -U $${POSTGRES_USER}"]

tobz

{
  "description": "A complete Vector configuration.",
  "$ref": "#/definitions/vector::config::builder::ConfigBuilder",
  "definitions": {
    "vector::config::builder::ConfigBuilder": {
      "description": "A complete Vector configuration.",
      "allOf": [
        {
          "type": "object",
          "properties": {

tobz

apm_config:
  # This points the `trace-agent` at Vector's `datadog_agent` source.
  apm_dd_url: http://localhost:8081
  
  # Set these to zero in order to avoid the Datadog Agent sampling traces before sending
  # them to Vector, which will skew computed statistics like request count, or success vs
  # error span count, etc.
  max_traces_per_second: 0
  errors_per_second: 0

tobz

// Our entrypoint for initializing logging via `tracing`.
pub fn initialize_logging(levels: &str) {
    // Here, `levels` is "vector=info,codec=info,vrl=info,file_source=info,tower_limit=trace,rdkafka=info,buffers=info,kube=info".
    let fmt_filter = tracing_subscriber::filter::Targets::from_str(levels).expect(
        "logging filter targets were not formatted correctly or did not specify a valid level",
    );

    // `MetricsLayer` impls `tracing_subscriber::layer::Layer` so this ends up being `Option<Filtered<MetricsLayer, ...>>`.
    let metrics_layer = metrics_layer_enabled()
        .then(|| MetricsLayer::new().with_filter(tracing_subscriber::filter::LevelFilter::INFO));

tobz

/// A period of time.
#[derive(Clone, Serialize, Deserialize)]
pub struct SpecialDuration(u64);

/// Controls the batching behavior of events.
#[derive(Clone)]
#[configurable_component]
pub struct BatchConfig {
    /// The maximum number of events in a batch before it is flushed.
    max_events: Option<u64>,

tobz

#[configurable(type = "sink")]
pub struct BasicStruct {
    foo: String,
}
const _: () = {
    #[automatically_derived]
    impl<'configurable> ::vector_config::Configurable<'configurable> for BasicStruct {
        fn shape() -> ::vector_config::Shape {
            ::vector_config::Shape::Boolean
        }

tobz

tracing = { version = "0.1.32", default-features = false }
tracing-core = { version = "0.1.23", default-features = false }
tracing-futures = { version = "0.2.5", default-features = false, features = ["futures-03"] }
tracing-log = { version = "0.1.2", default-features = false, features = ["log-tracer", "std"] }
tracing-subscriber = { version = "0.3.9", default-features = false, features = ["ansi", "env-filter", "fmt", "json", "registry"] }

tobz

use std::{time::Duration, marker::PhantomData};

use schemars::{JsonSchema, schema::{SchemaObject, InstanceType, SingleOrVec, NumberValidation, Schema}, gen::SchemaGenerator};
use serde::{Deserialize, Serialize, Serializer, Deserializer};
use serde_with::{SerializeAs, DeserializeAs, DurationSeconds, formats::Strict};

#[derive(Copy, Clone, Debug, Default)]
pub struct AsSchema<T: ?Sized>(PhantomData<T>);

impl<T: JsonSchema + ?Sized> AsSchema<T> {