Examples of org.apache.cassandra.utils.MerkleTree$TreeDifference

org.apache.cassandra.utils.MerkleTree
A MerkleTree implemented as a binary tree. A MerkleTree is a full binary tree that represents a perfect binary tree of depth 'hashdepth'. In a perfect binary tree, each leaf contains a sequentially hashed range, and each inner node contains the binary hash of its two children. In the MerkleTree, many ranges will not be split to the full depth of the perfect binary tree: the leaves of this tree are Leaf objects, which contain the computed values of the nodes that would be below them if the tree were perfect. The hash values of the inner nodes of the MerkleTree are calculated lazily based on their children when the hash of a range is requested with hash(range). Inputs passed to TreeRange.validate should be calculated using a very secure hash, because all hashing internal to the tree is accomplished using XOR. If two MerkleTrees have the same hashdepth, they represent a perfect tree of the same depth, and can always be compared, regardless of size or splits.

        oout.writeObject(mt);
        oout.close();


        ByteArrayInputStream bin = new ByteArrayInputStream(bout.toByteArray());
        ObjectInputStream oin = new ObjectInputStream(bin);
        MerkleTree restored = (MerkleTree)oin.readObject();
    
        // restore partitioner after serialization
        restored.partitioner(partitioner);


        assertHashEquals(initialhash, restored.hash(full));
    }

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    public void testDifference()
    {
        Range full = new Range(tok(0), tok(0));
        int maxsize = 16;
        mt.maxsize(maxsize);
        MerkleTree mt2 = new MerkleTree(partitioner, RECOMMENDED_DEPTH, maxsize);
        mt.init();
        mt2.init();


        TreeRange leftmost = null;
        TreeRange middle = null;
        TreeRange rightmost = null;


        // compact the leftmost, and split the rightmost
        Iterator<TreeRange> ranges = mt.invalids(full);
        leftmost = ranges.next();
        rightmost = null;
        while (ranges.hasNext())
            rightmost = ranges.next();
        mt.compact(leftmost.right);
        leftmost = mt.get(leftmost.right); // leftmost is now a larger range
        mt.split(rightmost.right);
        
        // set the hash for the left neighbor of rightmost
        middle = mt.get(rightmost.left);
        middle.hash("arbitrary!".getBytes());
        byte depth = middle.depth;


        // add dummy hashes to the rest of both trees
        for (TreeRange range : mt.invalids(full))
            range.addAll(new HIterator(range.right));
        for (TreeRange range : mt2.invalids(full))
            range.addAll(new HIterator(range.right));
        
        // trees should disagree for leftmost, (middle.left, rightmost.right]
        List<TreeRange> diffs = MerkleTree.difference(mt, mt2);
        assertEquals(diffs + " contains wrong number of differences:", 2, diffs.size());

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    {
        this(desc,
             initiator,
             // TODO: memory usage (maxsize) should either be tunable per
             // CF, globally, or as shared for all CFs in a cluster
             new MerkleTree(DatabaseDescriptor.getPartitioner(), desc.range, MerkleTree.RECOMMENDED_DEPTH, (int)Math.pow(2, 15)),
             gcBefore);
    }

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        // generate a tree
        Validator validator = new Validator(request);
        validator.prepare(store);
        validator.completeTree();
        MerkleTree ltree = validator.tree;


        // and a clone
        validator = new Validator(request);
        validator.prepare(store);
        validator.completeTree();
        MerkleTree rtree = validator.tree;


        // change a range in one of the trees
        Token ltoken = StorageService.getPartitioner().midpoint(local_range.left, local_range.right);
        ltree.invalidate(ltoken);
        MerkleTree.TreeRange changed = ltree.get(ltoken);

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    private void testTreeResponseWrite() throws IOException
    {
        AntiEntropyService.Validator v0 = new AntiEntropyService.Validator(Statics.req);
        IPartitioner part = new RandomPartitioner();
        MerkleTree mt = new MerkleTree(part, FULL_RANGE, MerkleTree.RECOMMENDED_DEPTH, Integer.MAX_VALUE);
        List<Token> tokens = new ArrayList<Token>();
        for (int i = 0; i < 10; i++)
        {
            Token t = part.getRandomToken();
            tokens.add(t);
            mt.split(t);
        }
        AntiEntropyService.Validator v1 = new AntiEntropyService.Validator(Statics.req, mt);
        DataOutputStream out = getOutput("service.TreeResponse.bin");
        AntiEntropyService.TreeResponseVerbHandler.SERIALIZER.serialize(v0, out, getVersion());
        AntiEntropyService.TreeResponseVerbHandler.SERIALIZER.serialize(v1, out, getVersion());

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            }
        });
        Range<Token> range = new Range<>(partirioner.getMinimumToken(), partirioner.getRandomToken());
        RepairJobDesc desc = new RepairJobDesc(UUID.randomUUID(), "Keyspace1", "Standard1", range);


        MerkleTree tree1 = createInitialTree(desc);
        MerkleTree tree2 = createInitialTree(desc);


        // difference the trees
        // note: we reuse the same endpoint which is bogus in theory but fine here
        TreeResponse r1 = new TreeResponse(ep1, tree1);
        TreeResponse r2 = new TreeResponse(ep2, tree2);

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    public void testDifference() throws Throwable
    {
        Range<Token> range = new Range<>(partirioner.getMinimumToken(), partirioner.getRandomToken());
        RepairJobDesc desc = new RepairJobDesc(UUID.randomUUID(), "Keyspace1", "Standard1", range);


        MerkleTree tree1 = createInitialTree(desc);
        MerkleTree tree2 = createInitialTree(desc);


        // change a range in one of the trees
        Token token = partirioner.midpoint(range.left, range.right);
        tree1.invalidate(token);
        MerkleTree.TreeRange changed = tree1.get(token);

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        assertEquals("Wrong differing ranges", interesting, new HashSet<>(diff.differences));
    }


    private MerkleTree createInitialTree(RepairJobDesc desc)
    {
        MerkleTree tree = new MerkleTree(partirioner, desc.range, MerkleTree.RECOMMENDED_DEPTH, (int)Math.pow(2, 15));
        tree.init();
        for (MerkleTree.TreeRange r : tree.invalids())
        {
            r.ensureHashInitialised();
        }
        return tree;
    }

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        // generate a tree
        Validator validator = new Validator(request);
        validator.prepare(store);
        validator.completeTree();
        MerkleTree ltree = validator.tree;


        // and a clone
        validator = new Validator(request);
        validator.prepare(store);
        validator.completeTree();
        MerkleTree rtree = validator.tree;


        // change a range in one of the trees
        Token ltoken = StorageService.getPartitioner().midpoint(local_range.left, local_range.right);
        ltree.invalidate(ltoken);
        MerkleTree.TreeRange changed = ltree.get(ltoken);

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        // empty validation
        AntiEntropyService.Validator v0 = new AntiEntropyService.Validator(Statics.req);


        // validation with a tree
        IPartitioner p = new RandomPartitioner();
        MerkleTree mt = new MerkleTree(p, FULL_RANGE, MerkleTree.RECOMMENDED_DEPTH, Integer.MAX_VALUE);
        for (int i = 0; i < 10; i++)
            mt.split(p.getRandomToken());
        AntiEntropyService.Validator v1 = new AntiEntropyService.Validator(Statics.req, mt);


        DataOutputStream out = getOutput("service.TreeResponse.bin");
        AntiEntropyService.Validator.serializer.serialize(v0, out, getVersion());
        AntiEntropyService.Validator.serializer.serialize(v1, out, getVersion());

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Related Classes of org.apache.cassandra.utils.MerkleTree$TreeDifference

org.apache.cassandra.dht.IPartitioner

org.apache.cassandra.dht.Range

org.apache.cassandra.dht.Token

org.apache.cassandra.repair.DifferencerTest

org.apache.cassandra.repair.LocalSyncTaskTest

org.apache.cassandra.repair.messages.ValidationComplete$ValidationCompleteSerializer

org.apache.cassandra.repair.Validator

org.apache.cassandra.repair.ValidatorTest

org.apache.cassandra.service.AntiEntropyService

org.apache.cassandra.service.AntiEntropyService$TreeRequestVerbHandler

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