To gain read performance, data model in Cassandra are denormalized to certain extend.
As denormalized produces duplicated record, how to avoid data anomalies?
As I know, there could be 2 ways to keep your data in different column family(CF) consistent:
Whenever you update row/column value in one CF, also update the
corresponding row/column in other CF.
Run some background batch
process to reconcile column values to make them consistent.
Which way you want to use, that may depends on how you did you model your data, and how strict is your application's requirement for data consistency among different CF.
jsfeng's answer is good on general principles, but a more specific answer is to use atomic batches: www.datastax.com/dev/blog/atomic-batches-in-cassandra-1-2
Related
I have a kind of requirement but not able to figure out how can I solve it. I have datasets in below format
id, atime, grade
123, time1, A
241, time2, B
123, time3, C
or if I put in list format:
[[123,time1,A],[124,timeb,C],[123,timec,C],[143,timed,D],[423,timee,P].......]
Now my use-case is to perform comparison, aggregation and queries over multiple row like
time difference between last 2 rows where id=123
time difference between last 2 rows where id=123&GradeA
Time difference between first, 3rd, 5th and latest one
all data (or last 10 records for particular id) should be easily accessible.
Also need to further do compute. What format should I chose for dataset
and what database/tools should I use?
I don't Relational Database is useful here. I am not able to solve it with Solr/Elastic if you have any ideas, please give a brief.Or any other tool Spark, hadoop, cassandra any heads?
I am trying out things but any help is appreciated.
Choosing the right technology is highly dependent on things related to your SLA. things like how much can your query have latency? what are your query types? is your data categorized as big data or not? Is data updateable? Do we expect late events? Do we need historical data in the future or we can use techniques like rollup? and things like that. To clarify my answer, probably by using window functions you can solve your problems. For example, you can store your data on any of the tools you mentioned and by using the Presto SQL engine you can query and get your desired result. But not all of them are optimal. Furthermore, usually, these kinds of problems can not be solved with a single tool. A set of tools can cover all requirements.
tl;dr. In the below text we don't find a solution. It introduces a way to think about data modeling and choosing tools.
Let me take try to model the problem to choose a single tool. I assume your data is not updatable, you need a low latency response time, we don't expect any late event and we face a large volume data stream that must be saved as raw data.
Based on the first and second requirements, it's crucial to have random access (it seems you wanna query on a particular ID), so solutions like parquet or ORC files are not a good choice.
Based on the last requirement, data must be partitioned based on the ID. Both the first and second requirements and the last requirement, count on ID as an identifier part and it seems there is nothing like join and global ordering based on other fields like time. So we can choose ID as the partitioner (physical or logical) and atime as the cluster part; For each ID, events are ordered based on the time.
The third requirement is a bit vague. You wanna result on all data? or for each ID?
For computing the first three conditions, we need a tool that supports window functions.
Based on the mentioned notes, it seems we should choose a tool that has good support for random access queries. Tools like Cassandra, Postgres, Druid, MongoDB, and ElasticSearch are things that currently I can remember them. Let's check them:
Cassandra: It's great on response time on random access queries, can handle a huge amount of data easily, and does not have a single point of failure. But sadly it does not support window functions. Also, you should carefully design your data model and it seems it's not a good tool that we can choose (because of future need for raw data). We can bypass some of these limitations by using Spark alongside Cassandra, but for now, we prefer to avoid adding a new tool to our stack.
Postgres: It's great on random access queries and indexed columns. It supports window functions. We can shard data (horizontal partitioning) across multiple servers (and by choosing ID as the shard key, we can have data locality on computations). But there is a problem: ID is not unique; so we can not choose ID as the primary key and we face some problems with random access (We can choose the ID and atime columns (as a timestamp column) as a compound primary key, but it does not save us).
Druid: It's a great OLAP tool. Based on the storing manner (segment files) that Druid follows, by choosing the right data model, you can have analytic queries on a huge volume of data in sub-seconds. It does not support window functions, but with rollup and some other functions (like EARLIEST), we can answer our questions. But by using rollup, we lose raw data and we need them.
MongoDB: It supports random access queries and sharding. Also, we can have some type of window function on its computing framework and we can define some sort of pipelines for doing aggregations. It supports capped collections and we can use it to store the last 10 events for each ID if the cardinality of the ID column is not high. It seems this tool can cover all of our requirements.
ElasticSearch: It's great on random access, maybe the greatest. With some kind of filter aggregations, we can have a type of window function. It can handle a large amount of data with sharding. But its query language is hard. I can imagine we can answer the first and second questions with ES, but for now, I can't make a query in my mind. It takes time to find the right solution with it.
So it seems MongoDB and ElasticSearch can answer our requirements, but there is a lot of 'if's on the way. I think we can't find a straightforward solution with a single tool. Maybe we should choose multiple tools and use techniques like duplicating data to find an optimal solution.
I've been trying to think about what the ideal table structure would be for the fastest Spark queries.
I'll try and provide a use case: Let's say your gathering stats for every car in the world and you want to use calculate various metrics with basic math (i.e. add, sub, mult, div).
Would be better to structure the data in a tall table with minimal fields like: day, metric, type, value?
Or would it be better to build a wide tables, that may store metrics independently. With more fields like: day, emmision_value, tire_pressure_value, speed_value, weight_value, heat_value, radio_value, etc .
Is it right to say that tall tables are better for spark? I assume it would be less memory intensive with a taller table.
As mentioned in the comments, this is a subjective question not exactly related to spark, but I'll try and answer none the less.
I assume it would be less memory intensive with a taller table.
Not really, the amount of storage required should be the same in either case based on the use case you have mentioned so let's get this out of the way. In case of taller tables there more rows and lesser columns and in case of wide tables the opposite. So on a cell level it should roughly be the same. I'm considering un compressed data independent of storage format.
Now lets talk about the mentioned use case. Simply put, it's aggregations. This may be fed downstream or may be used for reporting. Generally keeping this is mind, wider tables/views are better simply because - Lesser rows per day = less I/O as less shuffle.
Having said that, look through the cons below as well,
Schema evolution problems due to fixed schema
more suited for batch processing
Taller tables will be more streaming friendly, easier to extend for additional metrics and if its used with a source that supports push down, can result in quick partial scans.
in short, it very much depends on your operations.
We're investigating options to store and read a lot of immutable data (events) and I'd like some feedback on whether Cassandra would be a good fit.
Requirements:
We need to store about 10 events per seconds (but the rate will increase). Each event is small, about 1 Kb.
A really important requirement is that we need to be able to replay all events in order. For us it would be fine to read all data in insertion order (like a table scan) so an explicit sort might not be necessary.
Querying the data in any other way is not a prime concern and since Cassandra is a schema db I don't suppose it's possible when the events come in many different forms? Would Cassandra be a good fit for this? If so is there something one should be aware of?
I've had the exact same requirements for a "project" (rather a tool) a year ago, and I used Cassandra and I didn't regret. In general it fits very well. You can fit quite a lot of data in a Cassandra cluster and the performance is impressive (although you might need tweaking) and the natural ordering is a nice thing to have.
Rather than expressing the benefits of using it, I'll rather concentrate on possible pitfalls you might not consider before starting.
You have to think about your schema. The data is naturally ordered within one row by the clustering key, in your case it will be the timestamp. However, you cannot order data between different rows. They might be ordered after the query, but it is not guaranteed in any way so don't think about it. There was some kind of way to write a query before 2.1 I believe (using order by and disabling paging and allowing filtering) but that introduced bad performance and I don't think it is even possible now. So you should order data between rows on your querying side.
This might be an issue if you have multiple variable types (such as temperature and pressure) that have to be replayed at the same time, and you put them in different rows. You have to get those rows with different variable types, then do your resorting on the querying side. Another way to do it is to put all variable types in one row, but than filtering for only a subset is an issue to solve.
Rowlength is limited to 2 billion elements, and although that seems a lot, it really is not unreachable with time series data. Especially because you don't want to get near those two billions, keep it lower in hundreds of millions maximum. If you put some parameter on which you will split the rows (some increasing index or rounding by day/month/year) you will have to implement that in your query logic as well.
Experiment with your queries first on a dummy example. You cannot arbitrarily use <, > or = in queries. There are specific rules in SQL with filtering, or using the WHERE clause..
All in all these things might seem important, but they are really not too much of a hassle when you get to know Cassandra a bit. I'm underlining them just to give you a heads up. If something is not logical at first just fall back to understanding why it is like that and the whole theory about data distribution and the ring topology.
Don't expect too much from the collections within the columns, their length is limited to ~65000 elements.
Don't fall into the misconception that batched statements are faster (this one is a classic :) )
Based on the requirements you expressed, Cassandra could be a good fit as it's a write-optimized data store. Timeseries are quite a common pattern and you can define a clustering order, for example, on the timestamp of the events in order to retrieve all the events in time order. I've found this article on Datastax Academy very useful when wanted to learn about time series.
Variable data structure it's not a problem: you can store the data in a BLOB, then parse it internally from your application (i.e. store it as JSON and read it in your model), or you could even store the data in a map, although collections in Cassandra have some caveats that it's good to be aware of. Here you can find docs about collections in Cassandra 2.0/2.1.
Cassandra is quite different from a SQL database, and although CQL has some similarities there are fundamental differences in usage patterns. It's very important to know how Cassandra works and how to model your data in order to pursue efficiency - a great article from Datastax explains the basics of data modelling.
In a nutshell: Cassandra may be a good fit for you, but before using it take some time to understand its internals as it could be a bad beast if you use it poorly.
I have a 40 column RDBMS table which I am porting to Cassandra.
Using the estimator at http://docs.datastax.com/en/cassandra/2.1/cassandra/planning/architecturePlanningUserData_t.html
I created a excel sheet with column names, data types, size of each column etc.
The Cassandra specific overhead for each RDBMS row is a whopping 1KB when the actual data is only 192 bytes.
Since the overheads are proportional to number of columns, I thought it would be much better if I just create a UDT for the fields that are not part of the primary key. That way, I would incur the column overhead only once.
Also, I don't intend to run queries on inner fields of the UDT. Even if I did want that, Cassandra has very limited querying features that work on non PK fields.
Is this a good strategy to adopt? Are there any pitfalls? Are all these overheads easily eliminated by compression or some other internal operation?
On the surface, this isn't a bad idea at all. You are essentially abstracting your data by another level, but in a way that it is still manageable to meet your needs. It's actually good thinking.
I have a 40 column RDBMS table
This part slightly worries me. Essentially, you'd be creating a UDT with 40 properties. Not a huge deal in and of itself. Cassandra should handle that just fine.
But while you may not be querying on the inner fields of the UDT, you need to ask yourself how often you plan to update them. Cassandra stores UDTs as "frozen" types in a single column. This is important to understand for two reasons:
You cannot read a single property of a UDT without reading all properties of the UDT.
Likewise, you cannot update a single property in a UDT without rewriting all of them, either.
So you should keep that in mind while designing your application. As long as you won't be writing frequent updates to individual properties of the UDT, this should be a good solution for you.
I'm recently diving into Cassandra. However, there is no explicit documentation or ideas about pre defining column and data types. In a column family, cassandra enables dynamic column types like a document oriented database (MongoDb). However, cql enables to pre-define those column types with CREATE TABLE.
So, it's obvious that forcing column types would decrease the chance of invalid & wrong inserts.
Is there any other advantages doing pre defined column types ? For instance, is there a read performance increase if we have a pre-defined number of columns and their types ?
Because the schema is predefined you have to alter it before you can insert new rows. Using ALTER allowed for a number of performance enhancements that couldn't be achieved before such as reducing memory taken up by columns that are stored on heap memory.
This overhead is reduced on disk by compaction, but cant be done in memory (and it matters... because reading the memory cache is ofc faster than reading from disk). Handling this will:
reduce CPU usage
reduce memory usage
reduce disk space used
If you want the full technical details (including how the developers propose to implent the solution) take a look at the issue on Apache Cassandra's jira.
Just a note
The collections that are supported by Cassandra should cover use-cases where adding columns is required (for the sake of clarity I mean CQL columns) so having a static schema also forces the developer to think about their data model, and build it correctly.
I advise you to read this article by jbellis and all the comments that follow, it will clarify most of the points on why the static schema was enforced.