Hadoop HDFS Reviews & Product Details

Storage capability, reliability, robustness, scalability. Can access through different tools. Review collected by and hosted on G2.com.

Data stored in key and value format. Sometimes the query gets stuck. Review collected by and hosted on G2.com.

hadoop is one of the best softwares especially for cloud computing engineers. Review collected by and hosted on G2.com.

i face with so many problems when I want change my java version. seems silly but every time i wanna go to a higher version this happens to me. Review collected by and hosted on G2.com.

1) Distributed File System helps in partitioning huge data into multiple machines which helps in storing peta data,it follows write once and follows WORM-write ones read many times.

2) We have master node to distribute data among nodes and maintain the metadata of file version and path in it which is easier to spot the files.

3) Data Loss- As data is stored in multiple data nodes, there is a replication in case of any failure and very less chance to lose data.

4) Reading, copying, moving files to HDFS using putty commands is easier.

5) Apache ambari provides the user interface for Hadoop eco-systems which helps us to download,copy,rename,move and change permissions to directory and files in HDFS more easier.

6) Use of checksum for data integrity helps to check corruption of data. Review collected by and hosted on G2.com.

1) Failure in namenode has no replication which takes lot of time to recover.

2) As Block size has a limit in size,storing small files is not efficient.

3) It doesn't allow multiple users to write to a file. Review collected by and hosted on G2.com.

HDFS is fault tolerant as it makes multiple replica of the data which is stored in it, thus making it more reliable. Also when compared with traditional file systems, it is much robust and efficient in working on bulk amount of data, as it process via map reduce in the back-end. Also one of the major advantages is that HDFS can easily run on commodity hardware, making the initial setup cost very low. The commands used for HDFS to manage the files are almost the same as used in shell, providing an ease for the same Review collected by and hosted on G2.com.

One of the major drawbacks of Hadoop file system is that is the amount of data to be dealt with is less, it won't be efficient, as the time for processing small data is equivalent to time taken for bulk data.

By default, the security measures in Hadoop File system are disabled, making it insecure for data storage. Review collected by and hosted on G2.com.

Hadoop distributed file system is a distributed,scalable,fault tolerant and very efficient data storage platform. This is used to store data and can be used to support data processing frameworks like mapreduce and Spark. The best thing about hdfs is that it can be used by multiple things to create a solution. Best thing about hdfs and hadoop framework is that for training purposes we can even create single node cluster in our laptop. Review collected by and hosted on G2.com.

There are not much to dislike but speed is reduced when we deal with small files. if there are lot of small files to save then name node will be under pressure for saving the entry of those files. metadata will increase and hence performance will decrease. Review collected by and hosted on G2.com.

1.Storing of file in sequential format, using key value pair.- Stores file as key and content as value and encrypts them.

2.128 mb block size.- Previously it was 64 which was less. But still some people like the old block size.

3.storing multiple copies of data- Store same data in multiple nodes. It helps in case there is a failure of single node. As we all know sole purpose of hdfs is using commodity hardware and make the service available.

4. Horizontal scaling and distributed architecture- This helps the system to grow without worrying about previous data. You can increase the number of nodes in case of a drastic change in the amount of data. Review collected by and hosted on G2.com.

1.Map reduce jobs takes much time for smaller amount of data too. As the map and reduce jobs are getting created for any job.- Its recommended to use relational database in case of small amount of data in range of GB

2.The immutable nature- Files in HDFS can not be altered-more specifically it can not be modified. Append is allowed though. Review collected by and hosted on G2.com.

The increased block size gives hadoop advantage over others. Review collected by and hosted on G2.com.

Since the block size is large, it does not perform well with data sets lesser than the block size. Review collected by and hosted on G2.com.

Easy to use, easy to read, makes a lot of sense. Review collected by and hosted on G2.com.

There is nothing about hadoop I currently don't like Review collected by and hosted on G2.com.

HDFS or hadoop distributed file system is the storage component in Hadoop, where all the data resides at the end of the day. This is like a hard disk is to a computer, but actually this is a type of file system which allows user to store the data.

HDFS is very cost efficient. It is also fault tolerant as it makes replica of the data which is stored in it, thus maintaining a backup of all the files in commodity hardware. Review collected by and hosted on G2.com.

The major drawback of Hadoop is the lack of security measures taken, for sensitive information. This may not be considered purely for HDFS, but HDFS being a component of Hadoop, falls under this category.

Also it take time in processing small amount of data, thus making it not so robust for less data as compared to bulk data. Review collected by and hosted on G2.com.

HDFS is inexpensive because of two reasons. Firstly, the filesystem relies on commodity storage disks that are much less expensive than the storage media used for enterprise grade storage. Secondly, the filesystem shares the hardware with the computation framework as well, in this case, MapReduce. Also, HDFS is open source and does not levy licensing fee on the user.

HDFS has been around for more than 7 years and is considered mature technology. There is a large community behind it and a broad range of organizations that are storing petabytes of data on HDFS.

HDFS is optimized for MapReduce workloads. It provides very high performance for sequential reads and writes, which is the typical access pattern in MapReduce jobs. Review collected by and hosted on G2.com.

The main drawback of HDFS is that it is not POSIX compliant. This means HDFS is immutable, that is, files cannot be modified. Review collected by and hosted on G2.com.