we trying to build a data-ware house for our transaction system.
- We make 5000 -6000 transaction per day, they can go > 20,000.
- Each transaction produce a file, size (> 4MB)
we want to have a system, which can make updates to the existing data, consistent and availability, and have good read performance. Infrastructure is not any issue.
Hbase or cassandra or any other ? your help and guidance is highly appreciated.
Many thanks!
Most of newer nosql platform can do what you need in terms of performance - both hbase and cassandra scales horizontally (also Aerospike and others) so performances can be guaranteed if the data-model respect the "product-patterns" for data distribution.
I would not choose the technology in terms of performances.
What I would do is:
a list of different features offered by a bunch of products and then consider the one that, out of the box, best fit my needs
a list of operation I need to do on data and check if I am not going "against" some specific product
While 1 is easily done the 2 need a deep product analysis. For instance you say you need to update existing data -- let's imagine you choose Cassandra and you update very very frequently a column on which you put a secondary index (that, under the hood, creates a lookup table) for searching purpose. Any time you perform an update on this column on the lookup table a deletion and insertion is performed. You can read in this article that performing many deletes in Cassandra is considered an anti-pattern and can lead to problematic situations. This is just an example I did on Cassandra because is the one I know best among nosql products and not to tell you avoid Cassandra.
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 evaluating Cassandra to replace MySQL in our microservices environment, due to MySQL being the only portion of the infrastructure that is not distributed. Our needs are both write and read intensive as it's a platform for exchanging raw data. A type of "bus" for lack of better description. Our selects are fairly simple and should remain that way, but I'm already struggling to get past some basic filtering due to the extreme limitations of select queries.
For example, if I need to filter data it has to be in the key. At that point I can't change data in the fields because they're part of the key. I can use a SASI index but then I hit a wall if I need to filter by more than one field. The hope was that materialized views would help with this but in another post I was told to avoid them, due to some instability and problematic behavior.
It would seem that Cassandra is good at storage but realistically, not good as a standalone database platform for non-trivial applications beyond very basic filtering (i.e. a single field.) I'm guessing I'll have to accept the use of another front-end like Elastic, Solr, etc. The other option might be to accept the idea of filtering data within application logic, which is do-able, as long as the data sets coming back remain small enough.
Apache Cassandra is far more than just a storage engine. Its design is a distributed database oriented towards providing high availability and partition tolerance which can limit query capability if you want good and reliable performance.
It has a query engine, CQL, which is quite powerful, but it is limited in a way to guide user to make effective queries. In order to use it effectively you need to model your tables around your queries.
More often than not, you need to query your data in multiple ways, so users will often denormalize their data into multiple tables. Materialized views aim to make that user experience better, but it has had its share of bugs and limitations as you indicated. At this point if you consider using them you should be aware of their limitations, although that is generally good idea for evaluating anything.
If you need advanced querying capabilities or do not have an ahead of time knowledge of what the queries will be, Cassandra may not be a good fit. You can build these capabilities using products like Spark and Solr on top of Cassandra (such as what DataStax Enterprise does), but it may be difficult to achieve using Cassandra alone.
On the other hand there are many use cases where Cassandra is a great fit, such as messaging, personalization, sensor data, and so on.
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 been using Elasticsearch for quite sometime now and little experience using Cassandra.
Now, I have a project we want to use spark to process the data but I need to decide if we should use Cassandra or Elasticsearch as the datastore to load my data.
In terms of connector, both Cassandra and Elasticsearch now has a good connector to load the data so that won't be deciding factor.
The winning factor to decide will be how fast I can load my data inside Spark. My data is almost 20 terabytes.
I know I can run some test using JMeter and see the result myself but I would like to ask anyone familiar with both systems.
Thanks
The short exact answer is "it depends", mostly on cluster sizes =)
I wouldn't chose Elastisearch as a primary source for the data, because it's good at searching. Searching is a very specific task and it requires a very specific approach, which in this cases uses inverted index to store actual data. Each field basically goes into separate index and because of that the indexes are very compact. Although it's possible to store into index complete objects, such an index will hardly get any benefit of compression. That requires much more disk space to store indexes and much more cpu clocks, spinning disks to process them.
Cassandra on the other hand is pretty good at storing and retrieving data.
Without any more or less specific requirements, I'd say that Cassandra is good at being primary storage (and provides pretty simple search scenarios) and ES is good at searching.
I will refute Evgenii answer about how ES is only good at searching.
YES ES exceed at text search but it doesnt mean it can't do data.
You can actually treat it as if it was "Mongo" style Documentation and run "filter" queries on it to have fast fetch results. HOWEVER the question now becomes: how fast do you need your read/write and do you need any distributions? What ES lacks is distribution. Yes ES can do sharding but it has issues doing multi region distribution and reliability of replication of your data.
If you need the flexibility / reliability of your data I would swing to Cassanda. Also since you are dealing with TB - Cassandra might be a winner too because it is fitted for extreme volume.
If you need an easier time to to run searches (not limited to text search, eg: geo spacial you can do too) then ES might be a better fit. (note for the shear volume you are doing, you will need to shard in order to distribute your load).
Currently our system uses PostgreSQL, however we seem to have pushed the limit of its capabilities. Some of our tables need to handle over 100 read/write operations per second so it is probably time to scale horizontally across multiple machines.
Have a lot of experience using GAE's Big Table. Big Table had rich options for querying. For example, queries were possible against list data fields. Cassandra is supposed to be based off of Big Table, but if I understand correctly, for Cassandra, we will actually have to custom-code a layer on top of Cassandra that uses and maintains index tables.
Would be great if there was an open source database available for which we did not have to build our own custom logic for maintaining index tables, zig-zag merge joins, etc...
Is Cassandra a good candidate here? Or are there ones that might be considered better?
Unless the operations are huge joins or return hundreds of thousands of rows, any database you choose will be able to sustain 100 ops/s. Cassandra will have no problems serving thousands if not tens of thousands of reads and writes per node.
Without knowing more about your particular use case it's impossible to give you meaningful advice. Cassandra is a great database, but if it's right for you I don't know. I'd suggest looking through the cassandra tag here on Stack Overflow and look at what people ask about and if it looks at all like what you're trying to do, and if the answers say that it's possible with Cassandra (I know I've answered quite a few questions where the answer was that Cassandra wasn't the best choice for that particular case).
Cassandra and GAE Big Table have big similarities, but also big differences. One thing that trips up new Cassandra users is that there really isn't any way of doing things like "add this thing only unless that other thing was there" or "add an item and remove all but the last N items".