thanks having read the docs it appears to me that the main reason of hbase being faster is:
1. it behaves like an rdbms like oracle tetc. reads are looked for in the buffer cache for consistent reads and if not found then store files on disks are searched. Does this mean that this search is carried out through map-reduce on region servers? 2. when the data is written it is written to log file sequentially first, then to in-memory store, sorted like b-tree of rdbms and then flushed to disk. this is exactly what checkpoint in an rdbms does 3. one can point out that hbase is faster because log structured merge tree (LSM-trees) has less depth than a B-tree in rdbms. 4. all updates are done in memory o disk access 5. in summary LSM-trees reduce disk access when data is read from disk because of reduced seek time again less depth to get data with LSM-tree appreciate any comments cheers Dr Mich Talebzadeh LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* http://talebzadehmich.wordpress.com *Disclaimer:* Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction. On 21 October 2016 at 17:51, Ted Yu <yuzhih...@gmail.com> wrote: > See some prior blog: > > http://www.cyanny.com/2014/03/13/hbase-architecture- > analysis-part1-logical-architecture/ > > w.r.t. compaction in Hive, it is used to compact deltas into a base file > (in the context of transactions). Likely they're different. > > Cheers > > On Fri, Oct 21, 2016 at 9:08 AM, Mich Talebzadeh < > mich.talebza...@gmail.com> > wrote: > > > Hi, > > > > Can someone in a nutshell explain *the *Hbase use of log-structured > > merge-tree (LSM-tree) as data storage architecture > > > > The idea of merging smaller files to larger files periodically to reduce > > disk seeks, is this similar concept to compaction in HDFS or Hive? > > > > Thanks > > > > > > Dr Mich Talebzadeh > > > > > > > > LinkedIn * https://www.linkedin.com/profile/view?id= > > AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw > > <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCd > > OABUrV8Pw>* > > > > > > > > http://talebzadehmich.wordpress.com > > > > > > *Disclaimer:* Use it at your own risk. Any and all responsibility for any > > loss, damage or destruction of data or any other property which may arise > > from relying on this email's technical content is explicitly disclaimed. > > The author will in no case be liable for any monetary damages arising > from > > such loss, damage or destruction. > > > > > > > > On 21 October 2016 at 15:27, Mich Talebzadeh <mich.talebza...@gmail.com> > > wrote: > > > > > Sorry that should read Hive not Spark here > > > > > > Say compared to Spark that is basically a SQL layer relying on > different > > > engines (mr, Tez, Spark) to execute the code > > > > > > Dr Mich Talebzadeh > > > > > > > > > > > > LinkedIn * https://www.linkedin.com/profile/view?id= > > AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw > > > <https://www.linkedin.com/profile/view?id= > AAEAAAAWh2gBxianrbJd6zP6AcPCCd > > OABUrV8Pw>* > > > > > > > > > > > > http://talebzadehmich.wordpress.com > > > > > > > > > *Disclaimer:* Use it at your own risk. Any and all responsibility for > any > > > loss, damage or destruction of data or any other property which may > arise > > > from relying on this email's technical content is explicitly > disclaimed. > > > The author will in no case be liable for any monetary damages arising > > from > > > such loss, damage or destruction. > > > > > > > > > > > > On 21 October 2016 at 13:17, Ted Yu <yuzhih...@gmail.com> wrote: > > > > > >> Mich: > > >> Here is brief description of hbase architecture: > > >> https://hbase.apache.org/book.html#arch.overview > > >> > > >> You can also get more details from Lars George's or Nick Dimiduk's > > books. > > >> > > >> HBase doesn't support SQL directly. There is no cost based > optimization. > > >> > > >> Cheers > > >> > > >> > On Oct 21, 2016, at 1:43 AM, Mich Talebzadeh < > > mich.talebza...@gmail.com> > > >> wrote: > > >> > > > >> > Hi, > > >> > > > >> > This is a general question. > > >> > > > >> > Is Hbase fast because Hbase uses Hash tables and provides random > > access, > > >> > and it stores the data in indexed HDFS files for faster lookups. > > >> > > > >> > Say compared to Spark that is basically a SQL layer relying on > > different > > >> > engines (mr, Tez, Spark) to execute the code (although it has Cost > > Base > > >> > Optimizer), how Hbase fares, beyond relying on these engines > > >> > > > >> > Thanks > > >> > > > >> > > > >> > Dr Mich Talebzadeh > > >> > > > >> > > > >> > > > >> > LinkedIn * https://www.linkedin.com/profile/view?id= > > AAEAAAAWh2gBxianrbJ > > >> d6zP6AcPCCdOABUrV8Pw > > >> > <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrb > > >> Jd6zP6AcPCCdOABUrV8Pw>* > > >> > > > >> > > > >> > > > >> > http://talebzadehmich.wordpress.com > > >> > > > >> > > > >> > *Disclaimer:* Use it at your own risk. Any and all responsibility > for > > >> any > > >> > loss, damage or destruction of data or any other property which may > > >> arise > > >> > from relying on this email's technical content is explicitly > > disclaimed. > > >> > The author will in no case be liable for any monetary damages > arising > > >> from > > >> > such loss, damage or destruction. > > >> > > > > > > > > >
