According to most articles on internet Random Partitioning(RP) is better than Ordered Partitioning(OP) cause of the data distribution.
in fact, I think, that cause of data replication even if we are using the OP the data will be well distributed ! so is the first assumption is still true ?
what about reading performance ? is OP better than RP when trying to read data between two value in the same range ?
thanks a lot
I can't answer really answer confidently for HBase (which only supports Ordered Partitioning to my knowledge), but for Cassandra I would strongly discourage the use of OrderPreservingPartitioner and ByteOrderedPartitioner unless you have a very specific use case that requires it (like if you need to do range scans across keys). It is not very common for Ordered Partitioner to be used
in fact, I think, that cause of data replication even if we are using the OP the data will be well distributed ! so is the first assumption is still true ?
Not particularly, it is much more likely for hotspots to be encountered with an Ordered Partitioner vs. a Random Partitioner. As described from the Partitioners page on the Cassandra Wiki:
globally ordering all your partitions generates hot spots: some partitions close together will get more activity than others, and the node hosting those will be overloaded relative to others. You can try to mitigate with active load balancing but this works poorly in practice; by the time you can adjust token assignments so that less hot partitions are on the overloaded node, your workload often changes enough that the hot spot is now elsewhere. Remember that preserving global order means you can't just pick and choose hot partitions to relocate, you have to relocate contiguous ranges.
There are other problems with Ordered Partitioning that are described well here:
Difficult load balancing:
More administrative overhead is required to load balance the cluster. An ordered partitioner requires administrators to manually calculate partition ranges based on their estimates of the partition key distribution. In practice, this requires actively moving node tokens around to accommodate the actual distribution of data once it is loaded.
Uneven load balancing for multiple tables:
If your application has multiple tables, chances are that those tables have different row keys and different distributions of data. An ordered partitioner that is balanced for one table may cause hot spots and uneven distribution for another table in the same cluster.
With regards to:
what about reading performance ? is OP better than RP when trying to read data between two value in the same range ?
You will definitely achieve better performance for range scans (i.e. get all data between this key and that key).
So it really comes down to the kind of queries you are making. Are range scan queries between keys vital to you? In that case HBase may be a more appropriate solution for you. If it is not as important, there are reasons to consider C* instead. I won't add much more to that as I don't want my answer to devolve into comparing the two solutions :).
Related
I am using Apache Cassandra to store mostly time series data. And I am grouping the data and aggregating/counting it based on some conditions. At the moment I am doing this in a Java 8 application, but with the release of Cassandra 3.0 and the User Defined Functions, I have been asking myself if extracting the grouping and aggregation/counting logic to Cassandra is a good idea. To my understanding this functionallity is something like the stored procedures in SQL.
My concern is if this will impact the computation performance and the overall performance of the database. I am also not sure if there are other issues with it and if this new feature is something like the secondary indexes in Cassandra - you can do them, but it is not recommended at all.
Have you used user defined functions in Cassandra? Do you have any observations on the performance? What are the good and bad sides of this new functionality? Is it applicable in my use case?
You can compare it to using count() or avg() kind of aggregations. They can save you a lot of network traffic and object creation/GC by having the coordinator only send the result, but its easy to get carried away and make the coordinator do a lot of work. This extra work takes away from normal C* duties, and can just as likely increase GCs as reduce them.
If your aggregating 100 rows in a partition its probably fine and if your aggregating 10000 its probably not end of the world if its very rare. If your calling it once a second though its a problem. If your aggregating over 1000 I would be very careful.
If you absolutely need to do it and its a lot of data often, you may want to create dedicated proxy coordinators (-Djoin_ring=false) to bear the brunt of the load without impacting normal C* read/writes. At that point its just as easy to create dedicated workload DC for it or something (with RF=0 for your keyspace, and set application to be part of that DC with DCAwareRoundRobinPolicy). This also is the point where using Spark is probably the right thing to do.
Background:
I'm new to Cassandra and still trying to wrap my mind around the internal workings.
I'm thinking of using Cassandra in an application that will only ever have a limited number of nodes (less than 10, most commonly 3). Ideally each node in my cluster would have a complete copy of all of the application data. So, I'm considering setting replication factor to cluster size. When additional nodes are added, I would alter the keyspace to increment the replication factor setting (nodetool repair to ensure that it gets the necessary data).
I would be using the NetworkTopologyStrategy for replication to take advantage of knowledge about datacenters.
In this situation, how does partitioning actually work? I've read about a combination of nodes and partition keys forming a ring in Cassandra. If all of my nodes are "responsible" for each piece of data regardless of the hash value calculated by the partitioner, do I just have a ring of one partition key?
Are there tremendous downfalls to this type of Cassandra deployment? I'm guessing there would be lots of asynchronous replication going on in the background as data was propagated to every node, but this is one of the design goals so I'm okay with it.
The consistency level on reads would probably generally be "one" or "local_one".
The consistency level on writes would generally be "two".
Actual questions to answer:
Is replication factor == cluster size a common (or even a reasonable) deployment strategy aside from the obvious case of a cluster of one?
Do I actually have a ring of one partition where all possible values generated by the partitioner go to the one partition?
Is each node considered "responsible" for every row of data?
If I were to use a write consistency of "one" does Cassandra always write the data to the node contacted by the client?
Are there other downfalls to this strategy that I don't know about?
Do I actually have a ring of one partition where all possible values
generated by the partitioner go to the one partition?
Is each node considered "responsible" for every row of data?
If all of my nodes are "responsible" for each piece of data regardless
of the hash value calculated by the partitioner, do I just have a ring
of one partition key?
Not exactly, C* nodes still have token ranges and c* still assigns a primary replica to the "responsible" node. But all nodes will also have a replica with RF = N (where N is number of nodes). So in essence the implication is the same as what you described.
Are there tremendous downfalls to this type of Cassandra deployment?
Are there other downfalls to this strategy that I don't know about?
Not that I can think of, I guess you might be more susceptible than average to inconsistent data so use C*'s anti-entropy mechanisms to counter this (repair, read repair, hinted handoff).
Consistency level quorum or all would start to get expensive but I see you don't intend to use them.
Is replication factor == cluster size a common (or even a reasonable)
deployment strategy aside from the obvious case of a cluster of one?
It's not common, I guess you are looking for super high availability and all your data fits on one box. I don't think I've ever seen a c* deployment with RF > 5. Far and wide RF = 3.
If I were to use a write consistency of "one" does Cassandra always
write the data to the node contacted by the client?
This depends on your load balancing policies at the driver. Often we select token aware policies (assuming you're using one of the Datastax drivers), in which case requests are routed to the primary replica automatically. You could use round robin in your case and have the same effect.
The primary downfall will be increased write costs at the coordinator level as you add nodes. The maximum number of replicas written to I've seen is around 8 (5 for other data centers and 3 for local replicas).
In practice this will mean a reduced stability while performing large or batched writes (greater than 1mb) or a lower per node write TPS.
The primary advantage is you can do a lot of things that'd normally be awful and impossible to do. Want to use secondary indexes? probably will work reasonably well (assuming cardinality and partition size doesn't become your bottleneck there). Want to add a custom UDF that does GroupBy or use very large IN queries it'll probably work.
It is as #Phact mentions not a common usage pattern and I primarily saw it used with DSE Search on low write throughput use cases that had requirements for 'single node' features from Solr, but for those same use cases with pure Cassandra you'd get some benefits on the read side and be able to do expensive queries that are normally impossible in a more distributed cluster.
I am using cassandra 1.2.15 with ByteOrderedPartitioner in a cluster environment of 4 nodes with 2 replicas. I want to know what are the drawbacks of using the above partitioner in cluster environment? After a long search I found one drawback. I need to know what are the consequences of such drawback?
1) Data will not distribute evenly.
What type of problem will occur if data are not distributed evenly?
Is there is any other drawback with the above partitioner in cluster environment if so, what are the consequences of such drawbacks? Please explain me clearly.
One more question is, Suppose If I go with Murmur3Partitioner the data will distribute evenly. But the order will not be preserved, however this drawback can be overcome with cluster ordering (Second key in the primary keys). Whether my understanding is correct?
As you are using Cassandra 1.2.15, I have found a doc pertaining to Cassandra 1.2 which illustrates the points behind why using the ByteOrderedPartitioner (BOP) is a bad idea:
http://www.datastax.com/documentation/cassandra/1.2/cassandra/architecture/architecturePartitionerBOP_c.html
Difficult load balancing More administrative overhead is required to load balance the cluster. An ordered partitioner
requires administrators to manually calculate partition ranges
(formerly token ranges) based on their estimates of the row key
distribution. In practice, this requires actively moving node
tokens around to accommodate the actual distribution of data once
it is loaded.
Sequential writes can cause hot spots If your application tends to write or update a sequential block of rows at a time, then the
writes are not be distributed across the cluster; they all go to
one node. This is frequently a problem for applications dealing
with timestamped data.
Uneven load balancing for multiple tables If your application has multiple tables, chances are that those tables have different row keys and different distributions of data. An ordered
partitioner that is balanced for one table may cause hot spots and uneven distribution for another table in the same cluster.
For these reasons, the BOP has been identified as a Cassandra anti-pattern. Matt Dennis has a slideshare presentation on Cassandra Anti-Patterns, and his slide about the BOP looks like this:
So seriously, do not use the BOP.
"however this drawback can be overcome with cluster ordering (Second key in the primary keys). Whether my understanding is correct?"
Somewhat, yes. In Cassandra you can dictate the order of your rows (within a partition key) by using a clustering key. If you wanted to keep track of (for example) station-based weather data, your table definition might look something like this:
CREATE TABLE stationreads (
stationid uuid,
readingdatetime timestamp,
temperature double,
windspeed double,
PRIMARY KEY ((stationid),readingdatetime));
With this structure, you could query all of the readings for a particular weather station, and order them by readingdatetime. However, if you queried all of the data (ex: SELECT * FROM stationreads;) the results probably will not be in any discernible order. That's because the total result set will be ordered by the (random) hashed values of the partition key (stationid in this case). So while "yes" you can order your results in Cassandra, you can only do so within the context of a particular partition key.
Also, there have been many improvements in Cassandra since 1.2.15. You should definitely consider using a more recent (2.x) version.
I am trying to learn cassandra. One thing I am not clear is how to ask Cassandra to distribute various tables. ie say I have time series data coming into table t1,t2,t3
T1 is heavily loaded ( ratio is 2000: 2:4 for num of rows).
I want the data of T1 for a given day to be not on the same machine as T2 or T3; so my queries are equally distributed ie not put too much load on one machine.
Also as the data gets older, its queried less, how can I take into account this factor.
regards
Cassandra is automatically distributed, you do not have a direct control on how the data gets distributed. In most cases, by default it makes use of an md5 on the row key and depending on that selects which nodes (computers) will be used to save the data.
What you are talking about would be more of a planning for a standard SQL database. However, if you generate extremely large amount of statistical data that is only to be used by some backend processes and users, you could have a separate cluster of 2 or 3 nodes. That way your other tables would not be affected by those statistics.
However, the true power of Cassandra is to be used with one large cluster. If it slows down, add nodes to it and do the necessary repair to spread the data properly. That's it... pretty much.
As for the way a table is used, you can use all the parameters defined on a table to tweak its setup. If you mainly do writes to a table, then you can tweak the parameters to get faster writes and slower reads. The other way around is also available: one write, many reads. And also many writes and many reads. To tweak those settings, in most cases you will need to have your software running and gather various stats and make changes as time passes.
Update:
There is actually a solution, thinking about it, just... I never use that mode so I did not think about it.
When you use a cluster which supports sorted rows, you can use a specific row name and the data will then go to a specific node. Again, you do not directly have control over what goes where, but if you really really want to do it that way, that's probably the solution you are looking for.
In this case, the row name would start with a number such as 0x0001 for T1 data, and 0x0100 and 0x0200 for T2 and T3. Since you do not know where the data really goes and how Cassandra decides to use it, it is rather complicated to obtain the right results here. And if you change your cluster (i.e. add nodes) then all your assumptions of where the data goes may very well go to the toilet! (and that's not speaking of upgrading to a new version of Cassandra...)
I have created two node Cassandra cluster and try to perform load test. I find that one node or two node not making much difference in the through put I have supposed if 1 node can provide me 2000 tps for insert the two node should double the amount. Is it work like that?
if it is not then what actually Scaling means and how can I relate with it latency or throughput.
Cassandra is scalable. Just your case is a bit simplified since two nodes is not really the case of high scalability. You should be aware or the token partitioning algorithm used by Cassandra. As soon as you understand it, there should not be any quesitons. There is plenty of presentations about that. E.g. this one: http://www.datastax.com/resources/tutorials/partitioning-and-replication
In case of replication factor 1 everything is simple:
Each key-value pair you save/read from/to Cassandra is a query to one of Cassandra nodes in the cluster. Data is evenly distributed among nodes (see details of partitioning algorithm). So you always have total load evenly distributed among all nodes -> more nodes you have more load they can carry (and it is linear). In this case the system should of course be configured in a right way to avoid different kinds of network bottlenecks.
In case of replication factor more than 1 the situation is a bit more complicated, however the principle is the same.
There are lot of factors that contribute to this result.
A) check your replication factor. Although not desirable, in your case you can set it to 1
B) look into the shard in your primary key. If in your tests you are not changing it, then you are loading the data skewed and that the table is not scaling out to 2 nodes.
What does it mean when we say Casssandra is scalable?
There are basically two ways to scale a database.
Vertical scaling: Increasing the resources of the existing nodes in your cluster (more RAM, faster HDDs, more cores).
Horizontal scaling: Adding additional nodes to your cluster.
Vertical scaling tends to be more of a "band-aid" or temporary solution, because it has very finite limits. Your machines will only support so much RAM or so many cores, and once you max that out you really don't have anywhere to go.
Cassandra is "scalable" because it simplifies horizontal scaling. If you find that your existing nodes are maxing-out their available resources, you can simply add another node(s), adjust your replication factor, and run a nodetool repair. If you have had to do this with other database products, you will appreciate how (relatively) easy Cassandra makes it.
In your case, it's hard to know what exactly is going on without (a lot) more detail. But if your load tests are being adequately handled by your first node, then I can see why you wouldn't notice much of a difference by adding another.
If you haven't already, check out the Cassandra Stress Tool.
Additionally, be sure to check your current methods against this article, which is appropriately titled: How not to benchmark Cassandra