I meant, in the normal course of operation, rebalancing will not affect writes in flight. This is never an issue when pre splitting because, by definition, splits occurred before data was written to the regions.
"If I choose to automatically split rows, but choosing a row key like we described in this thread to keep data almost evenly distributed on every partition, I might end up having the increase in read/write latency when data is moving from a region to the other, although this could be rare, is this right?" Yes. Alok On Mon, Feb 23, 2015 at 10:11 AM, Marcelo Valle (BLOOMBERG/ LONDON) <mvallemil...@bloomberg.net> wrote: > Alok, just to clarify: > > When you say "Rebalancing shouldn't affect writes that are in flight." => you > mean just in the case I manually split the data on table creation right? > If I choose to automatically split rows, but choosing a row key like we > described in this thread to keep data almost evenly distributed on every > partition, I might end up having the increase in read/write latency when data > is moving from a region to the other, although this could be rare, is this > right? > > From: user@hbase.apache.org > Subject: Re: data partitioning and data model > > Assuming the cluster is not manually balanced, hbase will try to > maintain roughly equal number of regions on each region server. So, > when you "pre-split" a table, the regions should get evenly spread out > to all of the region servers. That said, if you are pre-splitting a > new table on a cluster that already has a lot of existing > tables/regions, then you may see uneven distribution of regions of the > new table. Hbase will try to keep the cluster wide region distribution > even across all tables, without taking into account the distribution > of regions of a specific table. > > Rebalancing shouldn't affect writes that are in flight. > > After a split and moving of a region, sometimes data locality between > the region server and the data node that hosts the region data files > is lost. If you have significant load on your cluster, you will notice > an increase in read/write latency in the traffic to these regions. The > locality will eventually return after the next major compaction. > > Links that have more details: > http://blog.cloudera.com/blog/2012/06/hbase-write-path/ > http://www.ngdata.com/visualizing-hbase-flushes-and-compactions/ > > Alok > > On Mon, Feb 23, 2015 at 8:42 AM, Marcelo Valle (BLOOMBERG/ LONDON) > <mvallemil...@bloomberg.net> wrote: >> Thanks Alok, >> >> I will take a good look at the link for sure. >> >> Just an additional question, I saw, reading this: >> http://stackoverflow.com/questions/13741946/role-of-datanode-regionserver-in-hbase-hadoop-integration >> That HBase can rebalance data inside region servers to keep cluster >> balanced. Does this happen also when using pre-loading? >> >> In the case of a rebalance, if I try to WRITE data to a record being >> rebalanced, would the write performance be affected? >> >> Best regards, >> Marcelo Valle. >> >> From: user@hbase.apache.org >> Subject: Re: data partitioning and data model >> >> You don't want a lot of columns in a write heavy table. HBase stores >> the "row key" along with each cell/column (Though old, I find this >> still useful: >> http://www.larsgeorge.com/2009/10/hbase-architecture-101-storage.html) >> Having a lot of columns will amplify the amount of data being stored. >> >> That said, if there are only going to be a handful of alert_ids for a >> given "user_id+timestamp" row key, then you should be ok. >> >> The query "Select * from table where user_id = X and timestamp > T and >> (alert_id = id1 or alert_id = id2)" can be accomplished with either >> design. See QualifierFilter and FuzzyRowFilter docs to get some ideas. >> >> Alok >> >> On Fri, Feb 20, 2015 at 11:21 AM, Marcelo Valle (BLOOMBERG/ LONDON) >> <mvallemil...@bloomberg.net> wrote: >>> Hi Alok, >>> >>> Thanks for the answer. Yes, I have read this section, but it was a little >>> too abstract for me, I think I was needing to check my understanding. Your >>> answer helped me to confirm I am on the right path, thanks for that. >>> >>> One question: if instead of using user_id + timestamp + alert_id I use >>> user_id + timestamp as row key, I would still be able to store alert_id + >>> alert_data in columns, right? >>> >>> I took the idea from the last section of this link: >>> http://www.appfirst.com/blog/best-practices-for-managing-hbase-in-a-high-write-environment/ >>> >>> But I wonder which option would be better for my case. It seems column >>> scans are not so fast as row scans, but what would be the advantages of one >>> design over the other? >>> >>> If I use something like: >>> Row key: user_id + timestamp >>> Column prefix: alert_id >>> Column value: json with alert data >>> >>> Would I be able to do a query like the one bellow? >>> Select * from table where user_id = X and timestamp > T and (alert_id = id1 >>> or alert_id = id2) >>> >>> Would I be able to do the same query using user_id + timestamp + alert_id >>> as row key? >>> >>> Also, I know Cassandra supports up to 2 billion columns per row (2 billion >>> rows per partition in CQL), do you know what's the limit for HBase? >>> >>> Best regards, >>> Marcelo Valle. >>> >>> From: aloksi...@gmail.com >>> Subject: Re: data partitioning and data model >>> >>> You can use a key like (user_id + timestamp + alert_id) to get >>> clustering of rows related to a user. To get better write throughput >>> and distribution over the cluster, you could pre-split the table and >>> use a consistent hash of the user_id as a row key prefix. >>> >>> Have you looked at the rowkey design section in the hbase book : >>> http://hbase.apache.org/book.html#rowkey.design >>> >>> Alok >>> >>> On Fri, Feb 20, 2015 at 8:49 AM, Marcelo Valle (BLOOMBERG/ LONDON) >>> <mvallemil...@bloomberg.net> wrote: >>>> Hello, >>>> >>>> This is my first message in this mailing list, I just subscribed. >>>> >>>> I have been using Cassandra for the last few years and now I am trying to >>>> create a POC using HBase. Therefore, I am reading the HBase docs but it's >>>> been really hard to find how HBase behaves in some situations, when >>>> compared to Cassandra. I thought maybe it was a good idea to ask here, as >>>> people in this list might know the differences better than anyone else. >>>> >>>> What I want to do is creating a simple application optimized for writes >>>> (not interested in HBase / Cassandra product comparisions here, I am >>>> assuming I will use HBase and that's it, just wanna understand the best >>>> way of doing it in HBase world). I want to be able to write alerts to the >>>> cluster, where each alert would have columns like: >>>> - alert id >>>> - user id >>>> - date/time >>>> - alert data >>>> >>>> Later, I want to search for alerts per user, so my main query could be >>>> considered to be something like: >>>> Select * from alerts where user_id = $id and date/time > 10 days ago. >>>> >>>> I want to decide the data model for my application. >>>> >>>> Here are my questions: >>>> >>>> - In Cassandra, I would partition by user + day, as some users can have >>>> many alerts and some just 1 or a few. In hbase, assuming all alerts for a >>>> user would always fit in a single partition / region, can I just use >>>> user_id as my row key and assume data will be distributed along the >>>> cluster? >>>> >>>> - Suppose I want to write 100 000 rows from a client machine and these are >>>> from 30 000 users. What's the best manner to write these if I want to >>>> optimize for writes? Should I batch all 100 k requests in one to a single >>>> server? As I am trying to optimize for writes, I would like to split these >>>> requests across several nodes instead of sending them all to one. I found >>>> this article: >>>> http://hortonworks.com/blog/apache-hbase-region-splitting-and-merging/ But >>>> not sure if it's what I need >>>> >>>> Thanks in advance! >>>> >>>> Best regards, >>>> Marcelo. >>> >>> >> >> > >
