reiser4: 1. internal tree

reiser4: 1. internal tree

This continues previous entry: introduction

0. B-trees overview

B-tree is am umbrella term for a variety of similar algorithms used to implement dictionary abstraction suitable in a form suitable for external storage. Reiser4 version of this algorithms, officially known as a dancing tree is closest to the B+-tree. Basically, in B+-tree user-supplied data records are stored in leaf nodes. Each leaf node keeps records from some particular key range (adjusted dynamically). Each leaf node is referenced from its parent: this reference is a pointer to the child (block number usually) and the smallest key in the key range covered by this leaf. Parent node contains multiple references to its children nodes, again from some particular key range. Parent node may have its own parent and so on until whole key space is covered.

Compare this with B-trees proper, where data records are stored at the levels other than leaf one. Also compare this with radix tree (aka PATRICIA) where key range covered by given node is uniquely determined by its level rather than adjusted dynamically as in B-trees.

Contents of reiser4 internal tree are initially stored on the disk. As file system operations go on, some blocks from the tree are read into memory and cached there. When new node is added to the tree, it first exists as a block-size buffer in memory. Under some conditions portions of tree are written out from memory back to the stable storage. During write-out, nodes that were marked dirty (either as a result of a modification, or because they are newly created), are written to the appropriate disk block and marked clean. Clean nodes can be discarded from memory.

1. lookup and modification

Lookup operation locates a record with a given key in a tree, or locates a place where such record will be inserted (this place is uniquely identified by a key) if it doesn't exist yet. All other tree operations start with lookup, because they have to find a place in a tree identified by given key.

Roughly speaking, lookup starts from the top of the tree, searches through index node to find it which of its children given key falls, loads that child node and repeats until it gets to the leaf level. One can say that lookup is top-to-bottom tree operation.

This poses two questions:
  • What is the top of a tree and how to find it?
  • How to find out which child node to proceed with?

First question seems trivial, because every tree has a root node, right? The problem is that, as will be explained below, concurrent tree modification operation may grow the tree by adding new root to it (so that old root becomes child of a new one), or shrink it by killing existing root. To avoid this, reiser4 tree has special imaginary node, existing only in memory that logically hangs just above root node. First of all, lookup locks this node and learns location of root node from it. After this, it loads root node in memory and releases the lock. This imaginary node is called uber-node. Curiously enough, Sun ZFS uses exactly the same term for the very similar thing. They even have 0x00babl0c magic number in their disk format, that is supposed to be as close as possible to the pronunciation of "uber block" (even more funny, "bablo" is Russian slang word for money, bucks).

To make search for a child node containing given key efficient, keys in the parent node are stored in an ordered array, so that binary search can be used. That binary search is (under many workloads) the single most critical CPU operation.

Now we known enough to code reiser4 lookup algorithm:
traverse_tree(key) {
        node     *parent; /* parent node */
        node     *child;  /* child node */
        position  ptr;    /* position of record within node */

        child = ubernode;
        ptr   = ubernode.root; /* put root block number in ptr */

        while (!node_is_leaf(child)) {
                parent = child;
                /* load node, stored at the given block, into memory */
                child = node_at(ptr);
                ptr = binary_search(child, key);
        return ptr;
Few comments:
  • position identifies a record within node. For leaf level this is actual record with user data, for non-leaf levels, record contains pointers to the child node.
  • note that lock on the child node is acquired before lock on the parent is released, that is, two locks are held at some moment. This is traditional tree traversal technique called lock coupling or lock crabbing.
  • traverse_tree() returns with ptr positioned at the record with required key (or at the place where such record would be inserted) and with appropriate leaf node locked.

As was already mentioned above, tree lookup is comparatively expensive (computationally, if nothing more) operation. At the same time, almost everything in reiser4 uses it extensively. As a result, significant effort has been put into making tree lookup more efficient. Algorithm itself doesn't leave much to be desired: its core is binary search and it can be optimized only that far. Instead, multiple mechanisms were developed that allows to bypass full tree lookup under some conditions. These mechanisms are:

1.1. seals

A seal is a weak pointer to a record in the tree. Every node in a tree maintains version counter, that is incremented on each node modification. After lookup for a given key was performed, seal can be created that remembers block number of found leaf node and its version counter at the moment of lookup. Seal verification process checks that node recorded in the seal is still in the tree and that its version counter is still the same as recorded. If both conditions are met, pointer to the record returned by lookup can still be used, and additional lookup for the same key can be avoided.

1.2. vroot

Higher-level file system object such as regular files and directories is represented as a set of tree records. Keys of these records are usually confined in some key range, and, due to the nature of B-trees, are all stored in the nodes having common ancestor node that is not necessary root. That is, records constituting given object are located in some subtree of reiser4 internal tree. Idea of vroot (virtual root) optimization is to track root of that sub-tree and to start lookups for object records from vroot rather than from real tree root. vroot is updated lazily: when lookup finds that tree was modified so that object subtree is no longer rooted at vroot, tree traversal restarts from real tree root and vroot is determined during descent.

Additional important advantage of vroot is that is decreases lock contention for the root node.

1.3. look-aside cache

Look-aside cache is simply a list of last N leaf nodes returned by tree lookup. This list is consulted before embarking into full-blown top-to-bottom traversal. This simple mechanism works due to locality of reference for tree accesses.

To be continued:
  • 2. concurrency control: lock ordering, hi-lo ordering
  • 3. eottl
  • 4. node format

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