Re: data partitioning and data model
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
Re: data partitioning and data model
I am sorry, consider I am using auto pre-splitting for question bellow. From: user@hbase.apache.org Subject: Re: data partitioning and data model 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
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
Re: data partitioning and data model
Thanks a lot! From: aloksi...@gmail.com Subject: Re: data partitioning and data model 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
Re: data partitioning and data model
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
Re: data partitioning and data model
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
Re: data partitioning and data model
Hi, Yes you would want to start your key by user_id. But you don’t need the timestamp. The user_id + alert_id should be enough on the key. If you want to get fancy… If your alert_id is not a number, you could use the EPOCH - Timestamp as a way to invert the order of the alerts so that the latest alert would be first. If your alert_id is a number you could just use EPOCH - alert_id to get the alerts in reverse order with the latest alert first. Depending on the number of alerts, you could make the table wider and store multiple alerts in a row… but that brings in a different debate when it comes to row width and how you use the data. On Feb 20, 2015, at 12:55 PM, Alok Singh aloksi...@gmail.com wrote: 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. smime.p7s Description: S/MIME cryptographic signature
Re: data partitioning and data model
Yes and no. Its a bit more complicated and it is also data dependent and how you’re using the data. I wouldn’t go too thin and I wouldn’t go to fat. On Feb 20, 2015, at 2:19 PM, Alok Singh aloksi...@gmail.com wrote: 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. smime.p7s Description: S/MIME cryptographic signature
data partitioning and data model
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.
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.
Re: data partitioning and data model
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.
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.
