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

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.


        // 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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    @Before
    public void clear()
    {
        TOKEN_SCALE = new BigInteger("8");
        partitioner = new RandomPartitioner();
        mt = new MerkleTree(partitioner, RECOMMENDED_DEPTH, Integer.MAX_VALUE);
    }

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    }


    @Test
    public void testSplitLimitDepth()
    {
        mt = new MerkleTree(partitioner, (byte)2, Integer.MAX_VALUE);


        assertTrue(mt.split(tok(4)));
        assertTrue(mt.split(tok(2)));
        assertEquals(3, mt.size());

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    }


    @Test
    public void testSplitLimitSize()
    {
        mt = new MerkleTree(partitioner, RECOMMENDED_DEPTH, 2);


        assertTrue(mt.split(tok(4)));
        assertEquals(2, mt.size());
        
        // should fail to split above maxsize

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        byte[] fullval = hashed(val, 5, 5, 4, 3, 2, 1);
        Range childfull = new Range(tok(0), tok(4));
        Range full = new Range(tok(0), tok(0));
        Range invalid = new Range(tok(4), tok(0));


        mt = new MerkleTree(partitioner, RECOMMENDED_DEPTH, Integer.MAX_VALUE);
        mt.split(tok(16));
        mt.split(tok(8));
        mt.split(tok(4));
        mt.split(tok(2));
        mt.split(tok(1));

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    public void testHashRandom()
    {
        int max = 1000000;
        TOKEN_SCALE = new BigInteger("" + max);


        mt = new MerkleTree(partitioner, RECOMMENDED_DEPTH, 32);
        Random random = new Random();
        while (true)
        {
            if (!mt.split(tok(random.nextInt(max))))
                break;

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    {
        TOKEN_SCALE = new BigInteger("16"); // this test needs slightly more resolution


        Range full = new Range(tok(0), tok(0));
        Iterator<TreeRange> ranges;
        MerkleTree mt2 = new MerkleTree(partitioner, RECOMMENDED_DEPTH, Integer.MAX_VALUE);


        mt.split(tok(8));
        mt.split(tok(4));
        mt.split(tok(12));
        mt.split(tok(6));
        mt.split(tok(10));
        
        ranges = mt.invalids(full);
        ranges.next().addAll(new HIterator(2, 4)); // (0,4]: depth 2
        ranges.next().addAll(new HIterator(6)); // (4,6]
        ranges.next().addAll(new HIterator(8)); // (6,8]
        ranges.next().addAll(new HIterator(/*empty*/ new int[0])); // (8,10]
        ranges.next().addAll(new HIterator(12)); // (10,12]
        ranges.next().addAll(new HIterator(14, 0)); // (12,0]: depth 2




        mt2.split(tok(8));
        mt2.split(tok(4));
        mt2.split(tok(12));
        mt2.split(tok(2));
        mt2.split(tok(10));
        mt2.split(tok(9));
        mt2.split(tok(11));


        ranges = mt2.invalids(full);
        ranges.next().addAll(new HIterator(2)); // (0,2]
        ranges.next().addAll(new HIterator(4)); // (2,4]
        ranges.next().addAll(new HIterator(6, 8)); // (4,8]: depth 2
        ranges.next().addAll(new HIterator(/*empty*/ new int[0])); // (8,9]
        ranges.next().addAll(new HIterator(/*empty*/ new int[0])); // (9,10]
        ranges.next().addAll(new HIterator(/*empty*/ new int[0])); // (10,11]: depth 4
        ranges.next().addAll(new HIterator(12)); // (11,12]: depth 4
        ranges.next().addAll(new HIterator(14, 0)); // (12,0]: depth 2


        byte[] mthash = mt.hash(full);
        byte[] mt2hash = mt2.hash(full);
        assertHashEquals("Tree hashes did not match: " + mt + " && " + mt2, mthash, mt2hash);
    }

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        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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org.apache.cassandra.dht.Range

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org.apache.cassandra.repair.DifferencerTest

org.apache.cassandra.repair.LocalSyncTaskTest

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

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org.apache.cassandra.service.AntiEntropyService

org.apache.cassandra.service.AntiEntropyService$TreeRequestVerbHandler

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