Ndb software architecture

I'm sure that someone else can describe the actual history of Ndb development much better, but here's my limited and vague understanding.

• Ndb is developed in an environment (Ericsson AXE telecoms switch) where Ericsson's PLEX is the language of choice
  PLEX supports multiple state machines (known as blocks) sending messages (known as signals) between them with some system-level conventions for starting up, restart and message classes. Blocks maintain internal state and define signal handling routines for different signal types. Very little abstraction within a block beyond subroutines is supported. (I'd love to hear some more detail on PLEX and how it has evolved). This architecture maps directly to the AXE processor design (APZ) which is unusual in having signal buffers implemented directly in silicon rather than software. This hard-coding drove Ndb's initial max supported signal size of 25 x 32-bit words.

• An emulated PLEX environment (VM) is made available on Unix systems, written in C++
  The VM runs as a Unix process. PLEX code for blocks is interpreted. Signals are routed between blocks by the VM. This allows development and deployment of PLEX based systems on standard Unix systems. It also allows Plex based systems to easily interact with Unix software. Each VM instance is a single threaded process routing incoming signals to the signal handling functions in each block class.
  

• A PLEX to C++ translation system is designed
  Blocks are mapped to large C++ classes with signal handling methods and per-block global state mapped to member variables. The limited labelling and abstraction encoded in the PLEX source are mapped to C style code within C++ classes.
  

• The VM environment is 'branched' from the original PLEX/AXE environment and starts to evolve independently as a base for Ndb.
  It offers access to more OS services such as communication, disk IO etc. Plex interpretation functionality is removed as all relevant Plex code has been mapped to native C++. VM instances can communicate with each other over various channels and form a distributed system.
  

• At some point in the timeline around here the Ndb team and product leave Ericsson

• Over time, common block functionality is abstracted into base and utility classes.
  Hardware and system-convention sourced constraints are eased, the level of abstraction is raised. New blocks are designed and implemented without a Plex heritage making use of C++ abstraction facilities. Existing blocks are refactored.
  

• Multi-threaded Ndbd (ndbmtd) is introduced, with groups of block instances running on different threads.
  Rather than being a radical design, it's a move back towards the original PLEX design point of 1 block instance per processor.

Today, Ndb executes a blocks communicating via signals model. Signals are no longer limited to 25 words. In single threaded Ndb (ndbd), all blocks share a single thread, with separate threads used for inter-VM communication setup and disk IO. In multi threaded Ndb (ndbmtd), block instances are grouped, and different functional groups share threads. In all cases, each block instance remains single-threaded, although the thread may be shared with other blocks.

The blocks and signals model is reminiscent of Erlang and Hoare's CSP – where concurrency is modelled as serial (or sequential) processes communicating with explicit messages, as opposed to a shared-memory model where communication occurs via memory with correctness controlled by locks, memory barriers and atomic instructions. It can also be considered similar to MPI and the Active object / Actor model.

Using explicit messaging for synchronisation/communication has costs – at runtime a given algorithm may require more data copying. At design time, potential concurrency must be explicitly designed-in with messaging and state changes. Mapping sequential algorithms to message passing state machines may require a bigger code transformation than mapping to a naive multithread safe shared-memory and locks implementation.

However I believe that these costs are generally paid off by the benefit of improved code clarity. Inter state-machine synchronisation becomes clearly visible, making synchronisation costs easier to visualise and understand. With explicit messaging as the main mechanism for inter-thread and inter-process communication, there is only a small kernel of multithreaded code to be implemented, proved correct and optimised. The bulk of the code can be implemented in single threaded style. There is no need for diverse libraries of multithread-optimised data structures. Processor and system architecture specific code and tradeoffs are minimised.

Internally, Ndb's VM supports only asynchronous messages between blocks. Using an asynchonous message passing style has many benefits. As the sending thread does not block awaiting a response to a message sent, it can work on other jobs, perhaps including the message just sent. This allows it to make the best use of warm instruction and data caches, reduces voluntary context switches and can reduce the likelihood of deadlock. Blocking IO (network, disk) is outsourced to a pool of threads. The signal processing thread(s) never block, except when no signals are available to process. The responsiveness of the system can be ensured by using prioritised job queues to determine the job to execute next and minimising the time spent processing individual jobs. From a formal point of view the number of possible multithreaded interactions is vastly reduced as thread-interleaving is only significant at signal processing boundaries. These limitations can make it easier to reason about the correctness and timing properties of the system.

However, coding in this asynchronous, event-driven style can be demanding. Any blocking operations (disk access, blocking communications, requests to other threads or processes etc.) must be implemented as an asynchronous request and response pair. This style can have an abstraction-dissolving property as many published data structures and algorithms are implemented assuming a synchronous model and making much use of the caller's stack for state storage and managing control flow. It can be difficult to design abstractions for the asynchronous style which don't leak so much messy detail as to be pointless. Additionally, the asynchronous style tends to flatten a system – as the need to return control to the lowest-level call point whenever concurrency is possible acts as a force against deep layers of abstraction. Side effects of this can include a tendency for error handling code to be non-localised to the source of the error. However, that is part of the charm of working on the system. The C++ environment gives a wide set of tools for designing such abstractions, and each improvement made simplifies future work.

Comments, corrections?