In my case I have a table structure like this:
table_1 {
entity_uuid text
,fk1_uuid text
,fk2_uuid text
,int_timestamp bigint
,cnt counter
,primary key (entity_uuid, fk1_uuid, fk2_uuid, int_timestamp)
}
The text columns are made up of random strings. However, only entity_uuid is truly random and evenly distributed. fk1_uuid and fk2_uuid have much lower cardinality and may be sparse (sometimes fk1_uuid=null or fk2_uuid=null).
In this case, I can either define only entity_uuid as the partition key or entity_uuid, fk1_uuid, fk2_uuid combination as the partition key.
And this is a LOOKUP-type of table, meaning we don't plan to do any aggregations/slice-dice based on this table. And the rows will be rotated out since we will be inserting with TTL defined for each row.
Can someone enlighten me:
What is the downside of having too many partition keys with very few
rows in each? Is there a hit/cost on the storage engine level?
My understanding is the cluster keys are ALWAYS sorted. Does that mean having text columns in a cluster will always incur tree
balancing cost?
Well you can tell where my heart lies by now. However, when all rows in a partition all TTL-ed out, that partition still lives, or is there a way they will be removed by the DB engine as well?
Thanks,
Bing
The major and possibly most significant difference between having big partitions and small partitions is the ability to do range scans. If you want to be able to do scan queries like
SELECT * FROM table_1 where entity_id = x and fk1_uuid > something
Then you'll need to have the clustering column for performance, otherwise this query would be difficult (a multi-get at best, full table scan at worst.) I've never heard of any cases where having too many partitions is a drag on performance but having too wide a partition (ie lots of clustering column values) can cause issues when you get into the 1B+ cell range.
In terms of the cost of clustering, it is basically free at write time (in memory sort is very very fast) but you can incur costs at read time as partitions become spread amongst various SSTables. Small partitions which are written once will not occur the merge penalty since they will most likely only exist in 1 SSTable.
TTL'd partitions will be removed but be sure to read up on GC_GRACE_SECONDS to see how Cassandra actually deals with removing data.
TL;DR
Everything is dependent on your read/write pattern
No Range Scans? No need for clustering keys
Yes Range Scans? Clustering keys a must
Related
I am working on the keyspace and tables for a Cassandra environment. I understand the size limitations of Cassandra and dealing with Partition keys to keep it optimized. However, I am having a disagreement with a developer regarding how to handle the keys. Is there any downside in having a key that would include a large number of data rather than a small amount of data. For example,
I have 100k records. I can create a key that will partition this into 10k; I could also create a key that will partition this into 10 records (by day). So either I store 10k and 10 partitions or 10 records and 10,000 partitions.
Keep in mind that having more columns in the key requires you to specify those columns in your select statements, which sometimes isn't desired. The more partitions the better - whether by picking a better single column or having multiple columns.
Cassandra reads data via the partition key, and can get help with performance if clustering columns are used. If you have a large partition, the entire partition must be read (memory and disk) and then merged for the output. If you have large partitions, this will definitely slow you down.
I'm inspecting the output of sstabledump, to gain better understanding of the cassandra data model, and I have some questions
From the output of of sstabledump it seems that
a table is a list of partitions (split by partition key)
a partition is a list of rows (split according to clustering key)
a row is a map of key-value pairs, where the keys belong in a predefined list
Question 1: For each partition, as well as for each row inside a partition, there is a position key. What does this value correspond to? Physical storage details? And how exactly?
Question 2: Each row inside each partition has a type: row key-value pair. Could this type be anything else? If yes, what? If not
why have a value that is always the same?
why is cassandra is classified as wide-column and other similar terms? Looks more like a two-level row storage.
Partition key is the murmur3 hash of whatever you assigned as the primary key. Consistent hashing is used with that hash to determine which node in the cluster that partition belongs to and its replicas. Within each partition data is sorted by clustering key, and then by cell name within the row. The structure is used so redundant things like timestamps if inserted for a row at once is only inserted once as a vint delta sequence from the partitions to save space.
On disk the partitions are sorted in order of this hashed key. The output of the position key is just referring to where in the sstable's data file its located (decompressed byte offset). type can also identify in that spot as a static block, which is located at the beginning of each partition for any static cells or a ranged tombstone marker (beginning or end). Note that values are sometimes for sstabledump repeated in json for readability even if not physically written on disk (ie repeated timestamps).
You can have many of these rows inside a partition, a common datamodel for time series for example is to use timestamp as the clustering key which makes very wide partitions with millions of rows. Pre 3.0 as well the data storage was closer to big table's design. It was essentially a Map<byte[], SortedMap<byte[], Cell>> where the Comparator of the sorted map was changed based on schema. It did not differentiate rows and columns within a partition, and it lead to massive amounts redundant data and was redesigned to fit the query language better.
Some more references:
Explanation of motivation of 3.0 change by DataStax here
Blog post by TLP has a good detailed explanation of the new disk format
CASSANDRA-8099
If I use a randomly generated unique Id , is it correct that
the cardinality would be rather large ?
If I have a key with a low cardinality like 5 category values that the partition key can take, and I want to distribute it, the recommended approach seems to be to make partition key into composite key.
But this requires that I have to specify all the parts of a composite key in my query to retrieve all records of that key.
Even then the generated token might end up being for the same node.
Is there any way to decide on a the additional column for composite key to that would guarantee that the data would be distributed ?
The thing is that with cassandra you actually want to have partitioning keys "known" so that you can access the data when you need it. I'm not sure what you mean when you say large cardinality on partitioning key. You would get a lot of partitions in the cluster. This is usually o.k.
If you want to distribute the data around the cluster. You can use artificial columns. And this approach is sometimes also called bucketing. Basically if you want to keep 100k+ or in never version 1 million+ columns it's o.k. to split this data into partitions.
Some people simply use a trick and when they insert the data they add some artificial bucket column to partition ... let's say random(1-10) and then when they are reading the data out they simply issue 10 queries or use an in operator and then fetch the data and merge it on the client side. This approach has many benefits in that it prevents appearance of "hot rows" in the cluster.
Chances for every key are more or less 1/NUM_NODES that it will end on the same node. So I would say most of the time this is not something you should worry about too much. Unless you have number of partitions that is smaller then the number of nodes in the cluster.
Basically there are two choices for additional column random (already described) or some function based on some input data i.e. when using time series data and you decide to bucket based on the month you can always calculate the month based on the data that you are going to insert and then you just put it in bucket. When you are retrieving the data then you know ... o.k. I'm looking something in May 2016 and then you know how to select the appropriate bucket.
Can someone give and show me how the data is layed out when you design your tables for wide vs. skinny rows.
I'm not sure I fully grasp how the data is spread out with a "wide" row.
Is there a difference in how you can fetch the data or will it be the same i.e. if it is ordered it doesn't matter if the data is vertical (skinny) or horizontally (wide) organized.
Update
Is a table considered with if the primary key consists of more than one column?
Or table will have wide rows only if the partition key is a composite partition key?
Wide... Skinny... Terms that make your head explode... I prefer to oversimplify the thing as such:
All the tables have wide rows
You simply need to take care of how wide the rows gets
This allows me to think this as follow (mangling a bit the C* terminology):
Number of RECORDS in a partition
1 <--------------------------------------- ... 2Billion
^ ^
Skinny rows wide rows
The lesser records in a partition, the skinner is the "partition", and vice-versa.
When designing for C* I always keep in mind a couple of things:
I want to use "skinny partitions" when my data can be fetched with one query and it is fully contained in one record of one partition. Typical example is something along SELECT * FROM table WHERE username = 'xmas79'; where the table has a primary key in the form of PRIMARY KEY (username)that let me get all the data belonging to a particular username.
I want to use "wide rows" when my data can be fetched with one query and it is fully contained on multiple records of one partition. Typical examples are range queries like SELECT * FROM table WHERE sensor = 'pressure' AND time >= '2016-09-22';, where the table has a primary key in the form of PRIMARY KEY (sensor, time).
So, first approach for one shot queries, second approach for range queries. Beware that this second approach have the (major) drawback that you can keep adding data to the partition, and it will get wider and wider, hurting performances.
In order to control how wide your partitions are, you need to add something to the partition key. In the sensor example above, if your don't violate your requirements of course, you can "group" some measurements by date, eg you split the measures in a day-by-day groups, making the primary key like PRIMARY KEY ((sensor, day), time), where the partition key was transformed to (sensor, day). By this approach, you have full (well, let's say good at least) control on the wideness of your partitions.
You only need to find a good compromise between your query capabilities and the desired performance.
I suggest these three readings for further investigation on the details:
Wide Rows in Cassandra CQL
Does CQL support dynamic columns / wide rows?
CQL3 for Cassandra experts
Beware that in the 1. there's a mistake in the second to last picture: the primary key should be
PRIMARY KEY ((user_id, tweet_id))
with double parenthesis around the columns instead of one.
I am in the process of learning Cassandra as an alternative to SQL databases for one of the projects I am working for, that involves Big Data.
For the purpose of learning, I've been watching the videos offered by DataStax, more specifically DS220 which covers modeling data in Cassandra.
While watching one of the videos in the course series I was introduced to the concept of splitting partitions to manage partition size.
My current understanding is that Cassandra has a max logical capacity of 2B entries per partition, but a suggested max of a couple 100s MB per partition.
I'm currently dealing with large amounts of real-time financial data that I must store (time series), meaning I can easily fill out GBs worth of data in a day.
The video course talks about introducing an additional partition key in order to split a partition with the purpose or reducing the size per partition requirement.
The video pointed out to using either a time based key or an arbitrary "bucket" key that gets incremented when the number of manageable rows has been reached.
With that in mind, this led me to the following problem: given that partition keys are only used as equality criteria (ie. point to the partition to find records), how do I find all the records that end up being spread across multiple partitions without having to specify either the bucket or timestamp key?
For example, I may receive 1M records in a single day, which would likely go over the 100-500Mb partition limit, so I wouldn't be able to set a partition on a per date basis, that means that my daily data would be broken down into hourly partitions, or alternatively, into "bucketed" partitions (for balanced partition sizes). This means that all my daily data would be spread across multiple partitions splits.
Given this scenario, how do I go about querying for all records for a given day? (additional clustering keys could include a symbol for which I want to have the results for, or I want all the records for that specific day)
Any help would be greatly appreciated.
Thank you.
Basically this goes down to choosing right resolution for your data. I would say first step for you would be to determinate what is best fit for your data. Lets for sake of example take 1 hour as something that is good and question is how to fetch all records for particular date.
Your application logic will be slightly more complicated since you are trading simplicity for ability to store large amounts of data in distributed fashion. You take date which you need and issue 24 queries in a loop and glue data on application level. However when you glue that in can be huge (I do not know your presentation or export requirements so this can pull 1M to memory).
Other idea can be having one table as simple lookup table which has key of date and values of partition keys having financial data for that date. Than when you read you go first to lookup table to get keys and then to partitions having results. You can also store counter of values per partition key so you know what amount of data you expect.
All in all it is best to figure out some natural bucket in your data set and add it to date (organization, zip code etc.) and you can use trick with additional lookup table. This approach can be used for symbol you mentioned. You can have symbols as partition keys, clustering per date and values of partitions having results for that date as values. Than you query for symbol # on 29-10-2015 and you see partitions A, D and Z have results so you go to those partitions and get financial data from them and glue it together on application level.