HA Overview

HA Overview

Some applications require Congress to be highly available. Some applications require a Congress Policy Engine (PE) to handle a high volume of queries. This guide describes Congress support for High Availability (HA) High Throughput (HT) deployment.

Please see the OpenStack High Availability Guide for details on how to install and configure OpenStack for High Availability.

HA Types

Warm Standby

Warm Standby is when a software component is installed and available on the secondary node. The secondary node is up and running. In the case of a failure on the primary node, the software component is started on the secondary node. This process is usually automated using a cluster manager. Data is regularly mirrored to the secondary system using disk based replication or shared disk. This generally provides a recovery time of a few minutes.

Active-Active (Load-Balanced)

In this method, both the primary and secondary systems are active and processing requests in parallel. Data replication happens through software capabilities and would be bi-directional. This generally provides a recovery time that is instantaneous.

Congress HAHT

Congress provides Active-Active for the Policy Engine and Warm Standby for the Datasource Drivers.

Run N instances of the Congress Policy Engine in active-active configuration, so both the primary and secondary systems are active and processing requests in parallel.

One Datasource Driver (DSD) per physical datasource, publishing data on oslo-messaging to all policy engines.

+-------------------------------------+      +--------------+
|       Load Balancer (eg. HAProxy)   | <----+ Push client  |
+----+-------------+-------------+----+      +--------------+
     |             |             |
PE   |        PE   |        PE   |        all+DSDs node
+---------+   +---------+   +---------+   +-----------------+
| +-----+ |   | +-----+ |   | +-----+ |   | +-----+ +-----+ |
| | API | |   | | API | |   | | API | |   | | DSD | | DSD | |
| +-----+ |   | +-----+ |   | +-----+ |   | +-----+ +-----+ |
| +-----+ |   | +-----+ |   | +-----+ |   | +-----+ +-----+ |
| | PE  | |   | | PE  | |   | | PE  | |   | | DSD | | DSD | |
| +-----+ |   | +-----+ |   | +-----+ |   | +-----+ +-----+ |
+---------+   +---------+   +---------+   +--------+--------+
     |             |             |                 |
     |             |             |                 |
        |                                 |
        |                                 |
+-------+----+   +------------------------+-----------------+
|  Oslo Msg  |   | DBs (policy, config, push data, exec log)|
+------------+   +------------------------------------------+


  • Datasource Drivers (DSDs):
    • One datasource driver per physical datasource
    • All DSDs run in a single DSE node (process)
    • Push DSDs: optionally persist data in push data DB, so a new snapshot can be obtained whenever needed
    • Warm Standby:
      • Only one set of DSDs running at a given time; backup instances ready to launch
      • For pull DSDs, warm standby is most appropriate because warm startup time is low (seconds) relative to frequency of data pulls
      • For push DSDs, warm standby is generally sufficient except for use cases that demand sub-second latency even during a failover
  • Policy Engine (PE):
    • Replicate policy engine in active-active configuration.
    • Policy synchronized across PE instances via Policy DB
    • Every instance subscribes to the same data on oslo-messaging
    • Reactive Enforcement: All PE instances initiate reactive policy actions, but each DSD locally selects a leader to listen to. The DSD ignores execution requests initiated by all other PE instances.
      • Every PE instance computes the required reactive enforcement actions and initiates the corresponding execution requests over oslo-messaging
      • Each DSD locally picks a PE instance as leader (say the first instance the DSD hears from in the asymmetric node deployment, or the PE instance on the same node as the DSD in a symmetric node deployment) and executes only requests from that PE
      • If heartbeat contact is lost with the leader, the DSD selects a new leader
      • Each PE instance is unaware of whether it is a leader
    • Node Configurations:
      • Congress supports the Two Node-Types (API+PE nodes, all-DSDs) node configuration because it gives reasonable support for high-load DSDs while keeping the deployment complexities low.
    • Local Leader for Action Execution:
      • Local Leader: every PE instance sends action-execution requests, but each receiving DSD locally picks a “leader” to listen to
      • Because there is a single active DSD for a given data source, it is a natural spot to locally choose a “leader” among the PE instances sending reactive enforcement action execution requests. Congress supports the local leader style because it avoids the deployment complexities associated with global leader election. Furthermore, because all PE instances perform reactive enforcement and send action execution requests, the redundancy opens up the possibility for zero disruption to reactive enforcement when a PE instance fails.
  • API:
    • Each node has an active API service
    • Each API service routes requests for the PE to its associated intranode PE
    • Requests for any other service (eg. get data source status) are routed to the Datasource and/or Policy Engine, which will be fielded by some active instance of the service on some node
  • Load balancer:
    • Layer 7 load balancer (e.g. HAProxy) distributes incoming API calls among the nodes (each running an API service).
    • load balancer optionally configured to use sticky session to pin each API caller to a particular node. This configuration avoids the experience of going back in time.
  • External components (load balancer, DBs, and oslo messaging bus) can be made highly available using standard solutions (e.g. clustered LB, HA rabbitMQ)

Performance Impact

  • Increased latency due to network communication required by multi-node deployment
  • Increased reactive enforcement latency if action executions are persistently logged to facilitate smoother failover
  • PE replication can achieve greater query throughput

Cautions and Limitations

  • Replicated PE deployment is new in the Newton release and a major departure from the previous model. As a result, the deployer may be more likely to experience unexpected issues.
  • In the Newton release, creating a new policy requires locking a database table. As a result, it should not be deployed with a database backend that does not support table locking (e.g., Galera). The limitation is expected to be removed in the Ocata release.
  • Different PE instances may be out-of-sync in their data and policies (eventual consistency). The issue is generally made transparent to the end user by configuring the load balancer to make each user sticky to a particular PE instance. But if a user reaches a different PE instance (say because of load balancer configuration or because the original instance went down), the end user reaches a different instance and may experience out-of-sync artifacts.
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