The config compiler initializes the scanner inside the [lexer](19-technical-concepts.md#technical-concepts-configuration-lexer)
stage.
The configuration files are parsed into memory from inside the [daemon CLI command](19-technical-concepts.md#technical-concepts-application-cli-commands-daemon)
which invokes the config validation in `ValidateConfigFiles()`. This compiles the
files into an AST expression which is executed.
At this stage, the expressions generate so-called "config items" which
are a pre-stage of the later compiled object.
`ConfigItem::CommitItems` takes care of committing the items, and doing a
rollback on failure. It also checks against matching apply rules from the previous run
and generates statistics about the objects which can be seen by the config validation.
`ConfigItem::CommitNewItems` collects the registered types and items,
and checks for a specific required order, e.g. a service object needs
a host object first.
The following stages happen then:
- **Commit**: A workqueue then commits the items in a parallel fashion for this specific type. The object gets its name, and the AST expression is executed. It is then registered into the item into `m_Object` as reference.
- **OnAllConfigLoaded**: Special signal for each object to pre-load required object attributes, resolve group membership, initialize functions and timers.
- **CreateChildObjects**: Run apply rules for this specific type.
- **CommitNewItems**: Apply rules may generate new config items, this is to ensure that they again run through the stages.
Note that the items are now committed and the configuration is validated and loaded
into memory. The final config objects are not yet activated though.
This only happens after the validation, when the application is about to be run
with `ConfigItem::ActivateItems`.
Each item has an object created in `m_Object` which is checked in a loop.
Again, the dependency order of activated objects is important here, e.g. logger features come first, then
config objects and last the checker, api, etc. features. This is done by sorting the objects
based on their type specific activation priority.
The following signals are triggered in the stages:
- **PreActivate**: Setting the `active` flag for the config object.
- **Activate**: Calls `Start()` on the object, sets the local HA authority and notifies subscribers that this object is now activated (e.g. for config updates in the DB backend).
## Features <a id="technical-concepts-features"></a>
Features are implemented in specific libraries and can be enabled
using CLI commands.
Features either write specific data or receive data.
Examples for writing data: [DB IDO](14-features.md#db-ido), [Graphite](14-features.md#graphite-carbon-cache-writer), [InfluxDB](14-features.md#influxdb-writer). [GELF](14-features.md#gelfwriter), etc.
Examples for receiving data: [REST API](12-icinga2-api.md#icinga2-api), etc.
The implementation of features makes use of existing libraries
and functionality. This makes the code more abstract, but shorter
and easier to read.
Features register callback functions on specific events they want
to handle. For example the `GraphiteWriter` feature subscribes to
new CheckResult events.
Each time Icinga 2 receives and processes a new check result, this
event is triggered and forwarded to all subscribers.
The GraphiteWriter feature calls the registered function and processes
the received data. Features which connect Icinga 2 to external interfaces
normally parse and reformat the received data into an applicable format.
objects. By default, all config objects are set to `HARunEverywhere` and
as such the object authority is true for any config object on any instance.
Specific objects can override and influence this setting, e.g. with `HARunOnce`
instead prior to config object activation.
This is done when the daemon starts and in a regular interval inside
the ApiListener class, specifically calling `ApiListener::UpdateObjectAuthority()`.
The algorithm works like this:
* Determine whether this instance is assigned to a local zone and endpoint.
* Collects all endpoints in this zone if they are connected.
* If there's two endpoints, but only us seeing ourselves and the application start is less than 60 seconds in the past, do nothing (wait for cluster reconnect to take place, grace period).
* Sort the collected endpoints by name.
* Iterate over all config types and their respective objects
* Ignore !active objects
* Ignore objects which are !HARunOnce. This means, they can run multiple times in a zone and don't need an authority update.
* If this instance doesn't have a local zone, set authority to true. This is for non-clustered standalone environments where everything belongs to this instance.
* Calculate the object authority based on the connected endpoint names.
* Set the authority (true or false)
The object authority calculation works "offline" without any message exchange.
Each instance alculates the SDBM hash of the config object name, puts that in contrast
modulo the connected endpoints size.
This index is used to lookup the corresponding endpoint in the connected endpoints array,
including the local endpoint. Whether the local endpoint is equal to the selected endpoint,
or not, this sets the authority to `true` or `false`.
TLS-Handshake timeouts occur if the server is busy with reconnect handling and other tasks which run in isolated threads. Icinga 2 uses threads in many ways, e.g. for timers to wake them up, wait for check results, etc.
In terms of the cluster communication, the following flow applies.
* The master initializes the connection in a loop through all known zones it should connect to, extracting the endpoints and their host/port attribute.
* This calls `AddConnection()` whereas a `Tcp::Connect()` is called to create a TCP socket.
* A new thread is spawned for future connection handling, this binds `ApiListener::NewClientHandler()`.
* On top of the TCP socket, a new TLS stream is created.
* The master performs a `TLS->Handshake()`
* Certificates are verified and the endpoint name is compared to the CN.
Everything runs through TLS, we don't use any "raw" connections nor plain message handling.
The TLS handshake and further read/write operations are not performed in a synchronous fashion
in the new client's thread. Instead, all clients share an asynchronous "event pool".
The TlsStream constructor registers a new SocketEvent by calling its constructor. It binds the
previously created TCP socket and itself into the created SocketEvent object.
`SocketEvent::InitializeEngine()` takes care of whether to use **epoll** (Linux) or
**poll** (BSD, Unix, Windows) as preferred socket poll engine. epoll has proven to be
faster on Linux systems.
The selected engine is stored as `l_SocketIOEngine` and later `Start()` ensures to do the following:
* Use a fixed number for creating IO threads.
* Create a `dumb_socketpair` which basically is a pipe from `in->out` and multiplexes the TCP socket
into a local Unix socket. This removes the complexity and slowlyness of the kernel dealing with the TCP stack and new events.
*`InitializeThread()` prepares epoll with `epoll_create`, socket descriptors and event mapping for later wakeup.
* Each event FD has its own "worker event thread" which deals with incoming data, called `ThreadProc` as endless loop.
By default, there are 8 of these worker threads.
In the `ThreadProc` loop, the following happens:
*`epoll_wait` gets called and provides an event whether new data is `ready` (via socket IO from the Kernel).
* The event created with `epoll_event` holds the `.fd.data` attribute which references the multiplexed event FD (and therefore tcp socket FD).
* All events in this cycle are stored with their descriptors in a list.
* Once the epoll loop is finished, the collected events are processed and the socketevent descriptor (which is the TlsStream object) calls `OnEvent()`.
#### On Socket Event State Machine <a id="technical-concepts-tls-network-io-async-socket-io-on-event"></a>
`OnEvent` implements the "state machine" depending on the current desired action. By default, this is `TlsActionNone`.
Once `TlsStream->Handshake()` is called, this initializes the current action to
`TlsActionHandshake` and performs `SSL_do_handshake()`. This function returns > 0
when successful, anything below needs to be dealt separately.
If the handshake was successful, the registered condition variable `m_CV` gets signalled
and the thread waiting for the handshake in `TlsStream->Handshake()` wakes up and continues
within the `ApiListener::NewClientHandler()` function.
Once the handshake is completed, current action is changed to either `TlsActionRead` or `TlsActionWrite`.
This happens in the beginning of the state machine when there is no action selected yet.
* **Read**: Received events indicate POLLIN (or POLLERR/POLLHUP as error, but normally mean "read").
* **Write**: The send buffer of the TLS stream is greater 0 bytes, and the received events allow POLLOUT on the event socket.
* Nothing matched: Change the event sockets to POLLIN ("read"), and return, waiting for the next event.
This also depends on the returned error codes of the SSL interface functions. Whenever `SSL_WANT_READ` occurs,
the event polling needs be changed to use `POLLIN`, vice versa for `SSL_WANT_WRITE` and `POLLOUT`.
In the scenario where the master actively connects to the clients, the client will wait for data and
change the event sockets to `Read` once there's something coming on the sockets.
Action | Description
---------------|---------------
Read | Calls `SSL_read()` with a fixed buffer size of 64 KB. If rc > 0, the receive buffer of the TLS stream is filled and success indicated. This endless loop continues until a) `SSL_pending()` says no more data from remote b) Maximum bytes are read. If `success` is true, the condition variable notifies the thread in `WaitForData` to wake up.
Write | The send buffer of the TLS stream `Peek()`s the first 64KB and calls `SSL_write()` to send them over the socket. The returned value is the number of bytes written, this is adjusted within the send buffer in the `Read()` call (it also optimizes the memory usage).
Handshake | Calls `SSL_do_handshake()` and if successful, the condition variable wakes up the thread waiting for it in `Handshake()`.
`SSL_WANT_READ` | The next event should read again, change events to `POLLIN`.
`SSL_ERROR_WANT_WRITE` | The next event should write, change events to `POLLOUT`.
`SSL_ERROR_ZERO_RETURN` | Nothing was returned, close the TLS stream and immediately return.
default | Extract the error code and log a fancy error for the user. Close the connection.
From this [question](https://stackoverflow.com/questions/3952104/how-to-handle-openssl-ssl-error-want-read-want-write-on-non-blocking-sockets):
```
With non-blocking sockets, SSL_WANT_READ means "wait for the socket to be readable, then call this function again."; conversely, SSL_WANT_WRITE means "wait for the socket to be writeable, then call this function again.". You can get either SSL_WANT_WRITE or SSL_WANT_READ from both an SSL_read() or SSL_write() call.
```
##### Successful TLS Actions
* Initialize the next TLS action to `none`. This re-evaluates the conditions upon next event call.
* If the stream still contains data, adjust the socket events.
* If the send buffer contains data, change events to `POLLIN|POLLOUT`.
* Otherwise `POLLIN` to wait for data.
* Process data when the receive buffer has them available and we are actively handling events.
* If the TLS stream is supposed to shutdown, close everything including the TLS connection.
#### Data Processing <a id="technical-concepts-tls-network-io-async-socket-io-data-processing"></a>
Once a stream has data available, it calls `SignalDataAvailable()`. This holds a condition
variable which wakes up another thread in a handled which was previously registered, e.g.
for JsonRpcConnection, HttpServerConnection or HttpClientConnection objects.
All of them read data from the stream and process the messages. At this point the string is available as JSON already and later decoded (e.g. Icinga data structures, as Dictionary).
### General Design Patterns <a id="technical-concepts-tls-network-io-design-patterns"></a>
Taken from https://www.ibm.com/developerworks/aix/library/au-libev/index.html
```
One of the biggest problems facing many server deployments, particularly web server deployments, is the ability to handle a large number of connections. Whether you are building cloud-based services to handle network traffic, distributing your application over IBM Amazon EC instances, or providing a high-performance component for your web site, you need to be able to handle a large number of simultaneous connections.
A good example is the recent move to more dynamic web applications, especially those using AJAX techniques. If you are deploying a system that allows many thousands of clients to update information directly within a web page, such as a system providing live monitoring of an event or issue, then the speed at which you can effectively serve the information is vital. In a grid or cloud situation, you might have permanent open connections from thousands of clients simultaneously, and you need to be able to serve the requests and responses to each client.
Before looking at how libevent and libev are able to handle multiple network connections, let's take a brief look at some of the traditional solutions for handling this type of connectivity.
### Handling multiple clients
There are a number of different traditional methods that handle multiple connections, but usually they result in an issue handling large quantities of connections, either because they use too much memory, too much CPU, or they reach an operating system limit of some kind.
The main solutions used are:
* Round-robin: The early systems use a simple solution of round-robin selection, simply iterating over a list of open network connections and determining whether there is any data to read. This is both slow (especially as the number of connections increases) and inefficient (since other connections may be sending requests and expecting responses while you are servicing the current one). The other connections have to wait while you iterate through each one. If you have 100 connections and only one has data, you still have to work through the other 99 to get to the one that needs servicing.
* poll, epoll, and variations: This uses a modification of the round-robin approach, using a structure to hold an array of each of the connections to be monitored, with a callback mechanism so that when data is identified on a network socket, the handling function is called. The problem with poll is that the size of the structure can be quite large, and modifying the structure as you add new network connections to the list can increase the load and affect performance.
* select: The select() function call uses a static structure, which had previously been hard-coded to a relatively small number (1024 connections), which makes it impractical for very large deployments.
There are other implementations on individual platforms (such as /dev/poll on Solaris, or kqueue on FreeBSD/NetBSD) that may perform better on their chosen OS, but they are not portable and don't necessarily resolve the upper level problems of handling requests.
All of the above solutions use a simple loop to wait and handle requests, before dispatching the request to a separate function to handle the actual network interaction. The key is that the loop and network sockets need a lot of management code to ensure that you are listening, updating, and controlling the different connections and interfaces.
An alternative method of handling many different connections is to make use of the multi-threading support in most modern kernels to listen and handle connections, opening a new thread for each connection. This shifts the responsibility back to the operating system directly but implies a relatively large overhead in terms of RAM and CPU, as each thread will need it's own execution space. And if each thread (ergo network connection) is busy, then the context switching to each thread can be significant. Finally, many kernels are not designed to handle such a large number of active threads.
```
### Alternative Implementations and Libraries <a id="technical-concepts-tls-network-io-async-socket-io-alternatives"></a>
While analysing Icinga 2's socket IO event handling, the libraries and implementations
below have been collected too. [This thread](https://www.reddit.com/r/cpp/comments/5xxv61/a_modern_c_network_library_for_developing_high/)
also sheds more light in modern programming techniques.
Our main "problem" with Icinga 2 are modern compilers supporting the full C++11 feature set.
Recent analysis have proven that gcc on CentOS 6 or SLES11 are too old to use modern
programming techniques or anything which implemens C++14 at least.
Given the below projects, we are also not fans of wrapping C interfaces into
C++ code in case you want to look into possible patches.
One key thing for external code is [license compatibility](http://gplv3.fsf.org/wiki/index.php/Compatible_licenses#GPLv2-compatible_licenses) with GPLv2.
Modified BSD and Boost can be pulled into the `third-party/` directory, best header only and compiled
