hflush: This API flushes all outstanding data (i.e. the current unfinished packet) from the client into the OS buffers on all DataNode replicas.
hsync: This API flushes the data to the DataNodes, like hflush(), but should also force the data to underlying physical storage via fsync (or equivalent). Note that only the current block is flushed to the disk device.
Use FileChannel.force(boolean) or FileDescriptor.sync() to force data to be persistent on disk. Either of them can work. FileChannel.force use FileDispacther.force and it calls fdatasync or fsync in Java 8.
When you use OutputStream.flush, it does not guarantee the data to be written to disk, just flush it to OS. Better to use FileOutputStream.getChannel().force(true) or FileOutputStream.getFD().sync() to guarantee the persistency, performance might not be good.
Special Thanks to Yongkun. He wrote very good blog post. 
Good tutorial for Bloom Filter understanding: http://billmill.org/bloomfilter-tutorial/
Bloom filters use case is following:
You have very large data sets that typically don’t fit in memory and you want to check your element it contains or not contains. Obviously It works very well for not contains detection.
if the bloom filter gives a hit: the item is probably inside
if the bloom filter gives a miss: the item is certainly not inside
How can I use in Java. Guava Provide a library for Bloom Filter:
m denotes the number of bits in the Bloom filter (bitSize)
n denotes the number of elements inserted into the Bloom filter (maxKeys)
k represents the number of hash functions used (nbHash)
e represents the desired false
positive rate for the bloom (err) If we fix the error rate (e) and know the number of entries, then the optimal bloom size
m = -(nln(err) / (ln(2)^2) ~= nln(err) / ln(0.6185)
The probability of false positives is minimized when k = m/n ln(2).
The value 31 was chosen because it is an odd prime. If it were even and the multiplication overflowed, information would be lost, as multiplication by 2 is equivalent to shifting. The advantage of using a prime is less clear, but it is traditional. A nice property of 31 is that the multiplication can be replaced by a shift and a subtraction for better performance: 31 * i == (i << 5) - i. Modern VMs do this sort of optimization automatically.
(from Chapter 3, Item 9: Always override hashcode when you override equals, page 48, Joshua Bloch’s Effective Java)
Snappy is a compression library that can be utilized by the native code.
It is currently an optional component, meaning that Hadoop can be built with
or without this dependency.
Download and compile snappy codecs. or you can install from your distro repo. I installed libsnappy and libsnappy-dev packages from Ubuntu repo. If everything is fine you can use -Drequire.snappy to fail the build if libsnappy.so is not found. If this option is not specified and the snappy library is missing,silently build a version of libhadoop.so that cannot make use of snappy. After than You just need to enter below command:
mvn clean package -Pdist,native -DskipTests -Dtar -Drequire.snappy
If you build snappy and It is located different place you can use this parameters
- -Dsnappy.prefix to specify a nonstandard location for the libsnappy header files and library files. You do not need this option if you have installed snappy using a package manager.
- -Dsnappy.lib to specify a nonstandard location for the libsnappy library files. Similarly to snappy.prefix, you do not need this option if you have installed snappy using a package manager.
- -Dbundle.snappy to copy the contents of the snappy.lib directory into the final tar file. This option requires that -Dsnappy.lib is also given, and it ignores the -Dsnappy.prefix option.
After compiling finished you can find your native libraries