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Elektra 0.11.0
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Elektra 0.11.0
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For an introduction, please read first about elektra classes. You might want to read about architecture first.
Data structures define the common layer in Elektra. They are used for transferring configuration between Elektra and applications, but also between plugins.
Both the KeySet and the interface to metadata within a Key are actually ADT (abstract data types). The API is designed so that different implementations of the data structures can be used internally.
A sorted array provides very fast iteration O(1) and has nearly no size-overhead.
A hash map data structure presents the best candidate for lookups O(1).
KeySet combines the best of both worlds: KeySet is implemented as a sorted array and uses an order-preserving minimal perfect hash map (OPMPHM) for lookups.
Application binary interface, or ABI, is the interface to all data structures of an application or library directly allocated or accessed by the user.
Special care has been taken in Elektra to support all changes within the data structures without any ABI changes. ABI changes would entail the recompilation of applications and plugins using Elektra. The functions keyNew(), ksNew() and kdbOpen() allocate the data structures for the applications. The user only gets pointers to them. It is not possible for the user to allocate or access these data structures directly when only using the public header file <kdb.h>. The functions keyDel(), ksDel() and kdbClose() free the resources after use. Using the C++ binding deallocation is done automatically.
The two basic Elektra datastructures Key and KeySet implement full copy-on-write (COW) functionality. If a key or a keyset gets copied, only a shallow copy with references to the original data (name, value, contained keys, etc.) is created. When this shared data is modified, new memory is allocated to keep the shared version in tact. As a consequence, duplicated keys or keysets only require a fraction of the memory compared to their source counterparts.
Read here.
This subsection describes what has changed between 0.7 and 0.8 and deals with some general implementation issues.
KeySet resembles the classical mathematical set. Operations like union, intersection or difference are well-defined. In mathematics typically every operation yields a new set. Instead, we try to reuse sets in the following ways:
A completely new and independent KeySet as return value would resemble the mathematical ideal closely. This operation would be expensive. Every Key needs to be duplicated and inserted into a new KeySet.
Such a deep duplication was only needed in kdbSet().
The resulting KeySet is created during the operation, but only a flat copy is made. This means that the keys in it are actually not duplicated, but only their reference counter is increased. This method is similar to the mathematical model. Compared with a deep copy it can achieve good performance. But all changes to the values of keys in the resulting KeySet affect the original KeySet, too.
ksDup(const KeySet *source) produces a new KeySet. That way the source is not changed as shown by the const modifier.
The result of the operation is applied to the KeySet passed as argument directly. This is actually quite common, but for this situation other names of the operations are more suitable.
For example, a union which changes the KeySet is called ksAppend().
A new KeySet is created, but the KeySet passed as parameter is reduced by the keys needed for the new KeySet. This is useful in situations where many operations have to be applied in a sequence reducing the given KeySet until no more keys are left. None of the reference pointers change in this situation.
ksCut(KeySet *ks, const Key *cutpoint) works that way. All keys below the cutpoint are moved from ks to the returned key set.
There are several ways to define consistency relations on key sets. For strict consistency every parent key must exist before the user can append a key to a key set. For example, the key set with the keys
would allow the key system:/elektra/mountpoints/tcl to be added, but not the key system:/apps/abc because system:/apps is missing. File systems enforce this kind of consistency.
These semantics are however not useful for configurations. Especially for user configurations often only some keys need to be overwritten. It is not a good idea to copy all parent keys to the users' configuration. For this reason we use a less strict definition of consistency supporting such holes.
We also evaluated a form of weak consistency. It avoids adding some unnecessary keys. A constraint is that a key can be added only if it has a parent key. But the constraint does not apply if no other key exists above the key about to be inserted. From that moment it will serve as parent key for other keys. With the current implementation of KeySet, however, it is not possible to decide this constraint in constant time. Instead its worst-case complexity would be $log(n) * x$ where $n$ is the number of keys currently in the key set and $x$ is the depth of the key. The depth is the number of / in the key name. The worst-case of the complexity applies when the inserting works without a parent key. For example, with the keys
the weak consistency would allow inserting user:/sw/apps/abc/current/bindings/key3 because it is directly below an existing key. It would also allow adding user:/sw/apps/xyz/current because it does not have any parent key. But it would not allow user:/sw/apps/abc/current/bindings/dir/key1 to add. The worst-case complexity was found to be too expensive, and hence KeySet has no consistency check at all.
This means any key with a valid key name can be inserted into KeySet. The KeySet is changed so that it is now impossible to append keys without a name. ksAppendKey(ks, Key *toAppend) takes ownership of the key toAppend and will delete the key in that case. The caller does not have to free toAppend: either it is in the key set or it is deleted.
Binary search determines the position where to insert a key. The C version of binary search bsearch() cannot tell where to insert a key when it is not found. So the algorithm has to be reimplemented. Java's binary search binarySearch() uses a trick to both indicate where a key is found and where to insert it with the same return code by returning the negative value ((-insertion point) - 1) indicating where the new value should be inserted when the key is not found. Elektra now also uses this trick internally.
Iterating over a KeySet is similar to iterating over arrays. With ssize_t ksGetSize (const KeySet *ks), the total number of Keys in a KeySet can be retrieved and with the function Key *ksAtCursor (const KeySet *ks, elektraCursor cursor) the Key * at the specified position cursor in ks can be retrieved. The first element (Key) has the index 0, so the last Key in the KeySet ks can be accessed with Key * k = ksAtCursor (ks, ksGetSize (ks) - 1). Please be aware the elements in a KeySet can move and therefore change their index, e.g. when deleting or adding elements or using ksCut ().
The OPMPHM is a non-dynamic randomized hash map of the Las Vegas type, that creates an index over the elements, to gain O(1) access.
The elements must be arranged in an array and each element must have a unique name, to identify the elements. The source can be found in kdbopmphm.h and opmphm.c and also works outside of Elektra.
The OPMPHM does not store any buckets, your array of elements are the buckets and the OPMPHM represent an arbitrary index over those. The desired index of an element, also known as the order, is set in OpmphmGraph->edges[i].order, where i is the i-th element in your array. When the orders should represent the array indices, the default order can be applied during the assignment step. When the orders are not the default order, OpmphmGraph->edges[i].order should be set before the assignment step.
Because the OPMPHM is non-dynamic, there are no insert and delete operations. The OPMPHM gets build for a static set of elements, once the OPMPHM is build, every:
leads to an invalid OPMPHM and forces a rebuild. A build consists of two steps the mapping step and the assignment step.
During the mapping step the OPMPHM maps each element to an edge in a random acyclic r-uniform r-partite hypergraph. In a r-uniform r-partite hypergraph each edge connects r vertices, each vertex in a different component. The probability of being acyclic and the number of mapping step invocations depends on the following variables:
r: The r variable defines the number of components in the random r-uniform r-partite hypergraph. Use the opmphmOptR (n) function to get an optimal value for your number of elements (n).c: The c variable defines the number of vertices in each component of the random r-uniform r-partite hypergraph. The number of vertices in one component is defined as (c * n / r) + 1, where n is the number of elements and r is the variable from above. The c variable must have a minimal value to ensure a success probability, use the opmphmMinC (r) function, with your r from above. To ensure an optimal time until success increment the c variable with the value from the opmphmOptC (n) function, where n is the number of elements.initSeed: The initial seed set in OpmphmInit->initSeed.opmphmOptR (n) and opmphmOptC (n) are heuristic functions constructed through benchmarks. Optimal is only one mapping step invocation in 99.5% of the observed cases. The benchmarks took arbitrary uniform distributed initial seeds and the heuristic functions are made to work with almost every seed.
Use opmphmNew () and opmphmGraphNew (...) to instantiate the needed structures. The function opmphmGraphNew (...) takes r and c as parameter. Use the opmphmOptR (...) function to get your r value, use this r also to get your c value the following way:
c = opmphmMinC (r) + opmphmOptC (n)
To initialize the OPMPHM build the OpmphmInit must be set with information about your data. Set your data array OpmphmInit->data and the element name extraction function OpmphmInit->getName, which should extract the string from a single data array entry. Provide a good seed in OpmphmInit->initSeed, needed in the next step.
The function opmphmMapping uses your seed (the OpmphmInit->seed will be changed) and tries to construct the random acyclic r-uniform r-partite hypergraph, this might not succeed, on cycles just call it again.
The opmphmAssignment () function assigns either your order (set at OpmphmGraph->edges[i].order) or a default order. The defaultOrder parameter indicates the behavior.
After the build the OpmphmInit and OpmphmGraph should be freed. The OPMPHM is now ready for constant lookups with the opmphmLookup ().
Once build, follow the steps from the build, just omit the opmphmNew () invocation.
1.9.1