If you google, you'll find benchmarks showing each to be faster than the other. In so far as there's any reality to which is faster in any given comparison, it seems to be a result of each incorporating ideas from the other, or at least going through development cycles to beat each other.
ORC is very fast for working with hive, and we use it at Handy. That said, the broader support for parquet might enable things like performing your own insertions into tables by dropping new files in there, or doing your own concatenation and cleanup. In summary, until you benchmark your own usage I'd assume performance is the same. If you're not going to benchmark, go by what's likely to be most convenient. On Sun, Mar 6, 2016 at 11:06 AM, Mich Talebzadeh <mich.talebza...@gmail.com> wrote: > Hi, > > Thanks for that link. > > It appears that the main advantages of Parquet is stated as and I quote: > > "Parquet is built to be used by anyone. The Hadoop ecosystem is rich with > data processing frameworks, and we are not interested in playing favorites. > We believe that an efficient, well-implemented columnar storage substrate > should be useful to all frameworks without the cost of extensive and > difficult to set up dependencies." > > Fair enough Parquet provides columnar format and compression. As I stated > I do not know much about it. However, my understanding of ORC is that it > provides better encoding of data, Predicate push down for some predicates > plus support for ACID properties. > > As Alan Gates stated before (Hive user forum, "Difference between ORC and > RC files" , 21 Dec 15) and I quote > > "Whether ORC is the best format for what you're doing depends on the data > you're storing and how you are querying it. If you are storing data where > you know the schema and you are doing analytic type queries it's the best > choice (in fairness, some would dispute this and choose Parquet, though > much of what I said above (about ORC vs RC applies to Parquet as well). If > you are doing queries that select the whole row each time columnar formats > like ORC won't be your friend. Also, if you are storing self structured > data such as JSON or Avro you may find text or Avro storage to be a better > format. > > So what would be the main advantage(s) of Parquet over ORC please besides > using queries that select whole row (much like "a row based" type > relational database does). > > > 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 > > > > On 6 March 2016 at 15:34, Uli Bethke <uli.bet...@sonra.io> wrote: > >> Curious why you think that Parquet does not have metadat at file, row >> group or column level. >> Please refer here to the type of metadata that Parquet supports in the >> docs http://parquet.apache.org/documentation/latest/ >> >> >> n 06/03/2016 15:26, Mich Talebzadeh wrote: >> >> Hi. >> >> I have been hearing a fair bit about Parquet versus ORC tables. >> >> In a nutshell I can say that Parquet is a predecessor to ORC (both >> provide columnar type storage) but I notice that it is still being used >> especially with Spark users. >> >> In mitigation it appears that Spark users are reluctant to use ORC >> despite the fact that with inbuilt Store Index it offers superior >> optimisation with data and stats at file, stripe and row group level. Both >> Parquet and ORC offer SNAPPY compression as well. ORC offers ZLIB as >> default. >> >> There may be other than technical reasons for this adaption, for example >> too much reliance on Hive plus the fact that it is easier to flatten >> Parquet than ORC (whatever that means). >> >> I for myself use either text files or ORC with Hive and Spark and don't >> really see any reason why I should adopt others like Avro, Parquet etc. >> >> Appreciate any verification or experience on this. >> >> Thanks >> , >> >> Dr Mich Talebzadeh >> >> >> >> LinkedIn * >> https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* >> >> >> >> <http://talebzadehmich.wordpress.com/>http://talebzadehmich.wordpress.com >> >> >> >> >> >> -- >> ___________________________ >> Uli Bethke >> Chair Hadoop User Group Irelandwww.hugireland.org >> HUG Ireland is community sponsor of Hadoop Summit Europe in Dublin >> http://2016.hadoopsummit.org/dublin/ >> >> > -- Want to work at Handy? Check out our culture deck and open roles <http://www.handy.com/careers> Latest news <http://www.handy.com/press> at Handy Handy just raised $50m <http://venturebeat.com/2015/11/02/on-demand-home-service-handy-raises-50m-in-round-led-by-fidelity/> led by Fidelity
