{ myLib, ... }: {
  kubernetes.resources = {
    deployments = {
      immich.spec = {
        selector.matchLabels = {
          app = "immich";
          component = "server";
        };

        strategy = {
          type = "RollingUpdate";

          rollingUpdate = {
            maxSurge = 0;
            maxUnavailable = 1;
          };
        };

        template = {
          metadata.labels = {
            app = "immich";
            component = "server";
          };

          spec = {
            volumes.data.persistentVolumeClaim.claimName = "data";

            enableServiceLinks = false;

            containers.immich = {
              image = "ghcr.io/immich-app/immich-server:v1.108.0";
              imagePullPolicy = "IfNotPresent";
              ports.web.containerPort = 3001;

              env = {
                TZ.value = "Europe/Amsterdam";
                REDIS_HOSTNAME.value = "redis.immich.svc.cluster.local";
                DB_HOSTNAME.value = "postgres.immich.svc.cluster.local";
                DB_USERNAME.value = "postgres";
                DB_PASSWORD.value = "ref+sops://secrets/kubernetes.yaml#/immich/databasePassword";
                DB_DATABASE_NAME.value = "immich";
                IMMICH_MACHINE_LEARNING_URL.value = "http://ml.immich.svc.cluster.local";
              };

              volumeMounts = [{
                name = "data";
                mountPath = "/usr/src/app/upload";
              }];
            };
          };
        };
      };

      ml.spec = {
        selector.matchLabels = {
          app = "immich";
          component = "machine-learning";
        };

        template = {
          metadata.labels = {
            app = "immich";
            component = "machine-learning";
          };

          spec = {
            volumes.cache.persistentVolumeClaim.claimName = "cache";

            containers.machine-learning = {
              image = "ghcr.io/immich-app/immich-machine-learning:v1.108.0";
              imagePullPolicy = "IfNotPresent";
              ports.ml.containerPort = 3003;
              env.MACHINE_LEARNING_WORKER_TIMEOUT.value = "600";

              volumeMounts = [{
                name = "cache";
                mountPath = "/cache";
              }];
            };
          };
        };
      };

      redis.spec = {
        selector.matchLabels = {
          app = "immich";
          component = "redis";
        };

        strategy = {
          type = "RollingUpdate";

          rollingUpdate = {
            maxSurge = 0;
            maxUnavailable = 1;
          };
        };

        template = {
          metadata.labels = {
            app = "immich";
            component = "redis";
          };

          spec = {
            containers.redis = {
              image = "docker.io/redis:6.2-alpine@sha256:d6c2911ac51b289db208767581a5d154544f2b2fe4914ea5056443f62dc6e900";
              ports.redis.containerPort = 6379;
              imagePullPolicy = "IfNotPresent";
            };
          };
        };
      };

      database.spec = {
        selector.matchLabels = {
          app = "immich";
          component = "database";
        };

        strategy = {
          type = "RollingUpdate";

          rollingUpdate = {
            maxSurge = 0;
            maxUnavailable = 1;
          };
        };

        template = {
          metadata.labels = {
            app = "immich";
            component = "database";
          };

          spec = {
            volumes.data.persistentVolumeClaim.claimName = "database";

            containers.postgres = {
              image = "docker.io/tensorchord/pgvecto-rs:pg14-v0.2.0@sha256:90724186f0a3517cf6914295b5ab410db9ce23190a2d9d0b9dd6463e3fa298f0";
              imagePullPolicy = "IfNotPresent";
              command = [ "postgres" ];
              args = [ "-c" "shared_preload_libraries=vectors.so" "-c" "search_path=\"$$user\", public, vectors" "-c" "logging_collector=on" "-c" "max_wal_size=2GB" "-c" "shared_buffers=512MB" "-c" "wal_compression=on" ];
              ports.postgres.containerPort = 5432;
              securityContext.runAsUser = 999;
              securityContext.runAsGroup = 999;

              env = {
                POSTGRES_PASSWORD.value = "ref+sops://secrets/kubernetes.yaml#/immich/databasePassword";
                POSTGRES_USER.value = "postgres";
                POSTGRES_DB.value = "immich";
                POSTGRES_INITDB_ARGS.value = "--data-checksums";
                PGDATA.value = "/pgdata/data";
              };

              volumeMounts = [{
                name = "data";
                mountPath = "/pgdata";
              }];
            };
          };
        };
      };
    };

    services = {
      server.spec = {
        type = "LoadBalancer";
        loadBalancerIP = myLib.globals.immichIPv4;

        selector = {
          app = "immich";
          component = "server";
        };

        ports.web = {
          port = 80;
          targetPort = "web";
        };
      };

      redis.spec = {
        selector = {
          app = "immich";
          component = "redis";
        };

        ports.redis = {
          port = 6379;
          targetPort = "redis";
        };
      };

      ml.spec = {
        selector = {
          app = "immich";
          component = "machine-learning";
        };

        ports.ml = {
          port = 80;
          targetPort = "ml";
        };
      };

      postgres.spec = {
        selector = {
          app = "immich";
          component = "database";
        };

        ports.postgres = {
          port = 5432;
          targetPort = "postgres";
        };
      };
    };

    persistentVolumeClaims.cache.spec = {
      accessModes = [ "ReadWriteOnce" ];
      resources.requests.storage = "5Gi";
    };
  };

  lab = {
    tailscaleIngresses.tailscale = {
      host = "immich";
      service.name = "server";
    };

    longhorn.persistentVolumeClaim = {
      data = {
        volumeName = "immich";
        storage = "50Gi";
      };

      database = {
        volumeName = "immich-db";
        storage = "5Gi";
      };
    };
  };
}