Version 0.9.1 has a few nice features and has reduced a lot of technical debt. The first features aren't that noticeable from a users point of view:
Java 1.6 support! This was contributed by Andrew Tolbert as he wanted to test against the 1.* branch of the C* Java driver which still supports Java 1.6. I hadn't intentionally dropped 1.6 support but I had brought in a jar that was compiled against 1.7 and thus wouldn't run in a 1.6 JVM. The offending jar was an internal C* jar that could help with serialisation so it was quite a lot of work to replace the use of this jar with custom serialisation code.
Another non-feature feature: Moving to Gradle and having a single build for the Server, Java client and Integration tests against all the versions of the C* Java driver. The aim of this was to make it MUCH easier for other people to contribute as before you had to install the server to a maven repo, then build the client and install it, then run the tests. Now you just run: ./gradlew clean check, Simples.
Real features
Verification of batch statements containing queries. Priming and prepared statements in batches will be in the next release.
Support for version 2.2 of the C* driver.
Verification of the prepare of prepared statements. This will allow you to test you only prepare the statements once and at the right time i.e. application start up.
Queries that contain variables are now captured properly. As of C* 2.0 you can use a prepared statement like syntax for queries and pass in variables. These are now captured as long as you primed the types of the variables (so Stubbed Cassandra knows how to parse them).
A farJar task so you build a standalone executable. Useful if you aren't using Stubbed Cassandra from a build tool like maven.
As always please raise issues or reach out to be on twitter if you have any questions or feedback.
Disclaimer: C* 3.0 is not released yet and all these examples are from a branch that hasn't even made it to trunk yet.
So this feature started off as "Global indexes", the final result is not a global index and I don't trust any claim of distributed indexes anyhow. If your data is spread across 200 machines, ad-hoc queries aren't a good idea reagardless of how you implement them as you will often end up going to all 200 machines.
Instead materialised views make a copy of your data partitioned a different way, which is basically what we've been doing manually in C* for years, this feature aims to remove the heavy lifting.
I'll use the same data model as the last article which is from the KillrWeather application. I will attempt to show use cases which we'd have previously used Spark or duplicated our data manually.
Recall the main data table:
This table assumes our queries are all going to be isolated to a weather station id (wsid) as it is the partition key. The KillrWeather application also has a table with information about each weather station:
I am going to denormalise by adding the columns from the weather_station table directly in the raw_weather_data table ending up with:
Now can we do some awesome things with materialised views? Of course we can!
So imagine you need to read the data not by weather station ID but by state_code. We'd normally have to write more code to duplicate the data manually. Not any more.
First let's insert some data, I've only inserted the primary key columns and one_hour_precip and I may have used UK county names rather than states :)
We can of course query by weather id and time e.g
We can then create a view:
We've asked C* is to materialise select country_code from raw_weather_data but with a different partition key, how awesome is that?? All of the original primary key columns and any columns in your new primary key are automatically added and I've added country_code for no good reason.
With that we can query by state and year as well. I included year as I assumed that partitioning by state would lead to very large partitions in the view table.
Where as secondary indexes go to the original table, which will often result in a multi partition query (a C* anti-pattern), a materialised view is copy of your data partitioned in a new way. This query will be as quick as if you'd duplicated the data your self.
The big take away here is that YOU, the developer, decide the partitioning of the materialised view. This is an important point. There was talk of you only needing to specify a cardinality, e.g low, medium, high or unique and leave C* to decide the partitioning. Where as that would have appeared more user friendly it would be a new concept and a layer of abstraction when IMO it is critical all C* developers/ops understand the importance of partitioning and we already do it every day for tables. You can now use all that knowledge you already have to design good primary keys for views.
The fine print
I'll use the term "original primary key" to refer to the table we're creating a materialised view on and MV primary key for our new view.
You can include any part of the original primary key in your MV primary key and a single column that was not part of your original primary key
Any part of the original primary key you don't use will be added to the end of your clustering columns to keep it a one to one mapping
If the part of your primary key is NULL then it won't appear in the materialised view
There is overhead added to the write path for each materialised view
Confused? Example time!
Original primary key: PRIMARY KEY ((wsid), year, month, day, hour)
Conclusion: No, this is actually the example above and it does not work as we tried to include two columns that weren't part of the original primary key. I updated the original primary key to: PRIMARY KEY ((wsid), year, month, day, hour, state_code) and then it worked.
Original primary key: PRIMARY KEY ((wsid), year, month, day, hour)
MV primary key: PRIMARY KEY ((state_code, year))
Conclusion: Yes - only one new column in the primary key: state_code
Here are some of the other questions that came up:
Is historic data put into the view on creation? Yes
Can the number of fields be limited in the new view? Yes - in the select clause
Is the view written to synchronously or asynchronously on the write path? Very subject to change! It's complicated! The view mutations are put in the batch log and sent out before the write, the write can succeed before all the view replicas have acknowledged the update but the batch log won't be cleared until a majority of them have responded. See the diagram in this article.
Are deletes reflected? Yes, yes they are!
Are updated reflected? Yes, yes they are!
What happens if I delete a table? The views are deleted
Can I update data via the view? No
What is the overhead? TBD, though it will be similar to using a logged batch if you had duplicated manually.
Will Patrick Mcfadin have to change all his data modelling talks? Well at least some of them
Combining aggregates and MVs? Oh yes
You can't use aggregates in the select clause for creating the materialised view but you can use them when querying the materialised view. So we can now answer questions like what is the total precipitation for a given year for a given state:
We can change our view to include the month in its key and do the same for monthly:
And then we can do:
Though my data had all the rain in one month :)
Conclusion
This feature changes no key concepts for C* data modelling it simply makes the implementation of the intended data model vastly easier. If you have data set that doesn't fit on a single server you're still denormalising and duplicating for performance and scalability, C* will just do a huge amount of it for you in 3.0.
My last post was on UDAs in C* 2.2 beta. C*2.2 is now at RC1 so again everything in this post is subject to change. I'm running off 3.0 trunk so it is even more hairy. Anyway there are more built in UDAs now so let's take a look...
I am going to be using the schema from KillrWeather to illustrate the new functionality. KillrWeather is a cool project that uses C* for its storage and a combination of Spark batch and Spark Streaming to provide analytics on weather data.
Now C* hasn't previously supported aggregates but 2.2 changes all that, so let's see which parts of KillrWeather we can ditch the Spark and go pure C*.
The raw weather data schema:
Spark batch is used to populate the high low "materialised view" table:
The code from KillrWeather Spark batch:
There's a lot going on here as this code is from a fully fledged Akka based system. But essentially it is running a Spark batch job against a C* partition and then using the Spark StatsCounter to work out the max/min temperature etc. This is all done against the raw table, (not shown) the result is passed back to the requester and asynchronously saved to the C* daily_aggregate table.
Stand alone this would look something like:
Now let's do something crazy and see if we can do away with this extra table and use C* aggregates directly against the raw data table:
Because we have the year, month, as clusterting columns we can get the max/min/avg all from the raw table. This will perform nicely as it is within a C* partition, don't do this across partitions! We haven't even had to define our own UDFs/UDAs as max and mean are built in. I wanted to analyse how long this UDA was taking but it currently isn't in trace so I raised a jira.
The next thing KillrWeather does is keep this table up to date with Spark streaming:
Can we do that with built in UDAs? Uh huh!
The data is a little weird as for one_hour_precip there are negative values, hence why it appears that we have less rain in a month than we do in a single day in that month.
We can also do things that don't include a partition key like get the max for all weather stations, but this will be slow / could cause OOM errors if you have a large table:
All the raw text for the queries are on my GitHub.
Disclaimer: all this was against 2.2-beta so the syntax may have changed.
Cassandra 2.2 introduced user defined functions and user defined aggregates. We can now do things like min, max and average on the server rather than having to bring all the data back to your application. Max and min are built in but we'll see how you could have implemented them your self.
Max/Min
Here's an example table for us to try and implement max/min against.
User defined aggregates work by calling your user defined function on every row returned from your query, they differ from a function because the first value to the function is state that is passed between rows, much like a fold.
Creating an aggregate is a two or three step process:
Create a function that takes in state (any Cassandra type including collections) as the first parameter and any number of additional parameters
(Optionally) Create a final function that is called after the state function has been called on every row
Refer to these in an aggregate
For max we don't need a final function but we will for average later.
Here we're using Java for the language (you can also use JavaScript) and just using Math.max. For our aggregate definition we start with (INITCOND) null (so it will return null for an empty table) and then set the state to be the max of the current state and the value passed in. We can our new aggregate like:
GroupBy
So there's no group by keyword in Cassandra but you can get similar behaviour with a custom user defined aggregate. Imagine you had a table that kept track of everything your customers did e.g
We can write a UDA to get a count of a particular column:
And we keep track of the counts in a map. Example use for counting both the event_type and the origin of the event:
More often than not when you use group by in other databases you are totalling another field. For example imagine we were keeping track of customer purchases and wanted a total amount each customer has spent:
We can create a generate aggregate for that called group_and_total:
And an example usage:
As you can see Haddad spends way too much.
Average
The Cassandra docs have an example of how to use a user defined aggregate to calculate aggregates: http://cassandra.apache.org/doc/cql3/CQL-2.2.html#udas
Small print
If you've ever heard me rant about distributed databases you've probably heard me talk about scalable queries, ones that work on a 3 node cluster as well as a 1000 node cluster. User defined functions and aggregates are executed on the coordinator. So if you don't include a partition key in your query all the results are brought back to the coordinator for your function to be executed, if you do a full table scan for your UDF/A don't expect it to be fast if your table is huge.
This functionality is in beta for a very good reason, it is user defined code running in your database! Proper sandboxing e.g with a SecurityManager will be added before this goes mainstream.
I am a huge test first advocate. Since seeing Martin Thompson speak I am now trying to include performance testing with the same approach. I am going to call this approach Performance, Acceptance and Unit Test Driven Development, or PAUTDD :)
Tools/frameworks/library come and go so I'll start with the theory then show how I set this up using Junit and Mockio for unit testing, Cucumber for acceptance tests and Gatling for performance tests. Well I won't show JUnit and Mockito because that is boring!
So here's how I develop a feature:
Write a high level end to end acceptance test. There will be times where I'll want acceptance tests not to be end to end, like if there was an embedded rules engine.
Write a basic performance test.
TDD with unit testing framework until the above two pass.
Consider scenario based performance test.
I hate to work without test first at the acceptance level, even for a small feature (half a day dev?) I find them invaluable for keeping me on track and letting me know when functionally I am done. Especially if I end up doing a bit too much Unit testing/Mocking (bad Chris!) as when head down in code it is easy to forget the big picture: what functionality are you developing for a user.
Next is a newer part of my development process. Here I want a performance test for the new feature, this may not be applicable but it usually is. Which ever framework I am using here I want to be able to run it locally and as part of my continuous integration for trend analysis. However I want more for my effort than that, I really want to be able to use the same code for running against various environments running different hardware.
I hate performance tests that aren't in source control and versioned. Tools that use a GUI are no use to me, I constantly found while working at my last company that the "performance testers" would constantly change their scripts and this would change the trend way more than any application changes. I want to be able to track both.
So how to set all this up for a Spring Boot application using Cucumber and Gatling?
This post is on build setup, not the actual writing of the tests. My aim is to enable easy acceptance/performance testing.
Here's the layout of my latest project:
Main is the source, test is the unit tests and e2e is both the Cucumber and the Gatling tests. I could have had separate source sets for the Cucumber and Gatling tests but that would have confused IntelliJ's Gradle support too much (and they are nicely split by Cucumber being Java and Gatling being Scala).
Cucumber JVM
There are various articles on Cucumber-JVM, here's the steps I used to get this running nicely in the IDE and via Gradle.
First the new source set:
Nothing exciting here, we are using the same classapth as test, we could have had a separate one.
Next is dependencies, this is actually the Gatling and HTTP client (for hitting our application) as well.
We cucumber JUnit and Spring dependencies.
Next is the source code for the acceptance tests. The Features are in the resource folder and the source is in the Java folder. To allow running via Gradle you also create a JUnit test to run all the features. Intellij should work find without this by just running the feature files.
Here I have Features separated by type, here's an example Feature:
I like to keep the language at a high level so a non-techy can write these. The JUnit test RunEndToEndTests looks like this:
This is what Gradle will pick up when we run this from the command line. You could separate this out into multiple tests if you wanted.
For running inside IntelliJ you might need to edit the run configuration to include a different Glue as by default it will be the same as the package your Feature file is in, for this project this wouldn't pick up the GlobalSteps as it is outside of the security/users folder. This is what my configuration looks like, I set this as the default:
Now our features will run if you want to see what the implementation of the Steps look like, checkout the whole project from Github.
Gatling
This is my first project using Gatling, I wanted my scenarios in code that I could have in version control. Previously I've used JMeter. Where as you can checkin the XML it really isn't nice to look at in diff tools. I've also been forced *weep* to use more GUI based tools like SOASTA and HP Load runner. One thing I haven't looked at is Gatling's support for running many agents. For me to continue using Gatling beyond developer trend analysis this needs to be well supported.
We already have the dependencies, see the dependencies section above, and we're going to use the same source set. The only difference is we're going to be writing these tests in Scala.
My first requirement was not to have to have Gatling installed manually on developer and CI boxes. Here's how to do this in Gradle:
Where BasicSimulation is the fully qualified name of my Gatling load test. All we do here is define a JavaExec task with the Gatling main class, tell Gatling where our source is and which simulation to run.
To tie all this together so it runs every time we fun Gradle check we add the following at the bottom of our Gradle build file:
This will produce reports that can be published + consumed by the Jenkins Gatling plugin.
To run the same load test from Intellij we do exactly the same in a run configuration:
A basic Gatling tests looks like this:
This test will run a very simple test where a single virtual user hits the /api/auction URL 100 times with a pause of 10 milliseconds. The top couple of lines start the Spring boot application and register a shut down hook to stop it.
We'll then end up with a report that looks like this:
This is a pretty terrible load test as it runs for 2 seconds and has a single user. But the point of this post is to setup everything so when adding new functionality it is trivial to add new performance and acceptance tests.
That's it, happy testing! If you want to see the whole project is on Github. It us under active development so you'll know how I got on with Gatling based if it is still there and there are lots of tests that use it!
Last week I gave a webinar on avoiding anti-patterns in Cassandra. It was good fun to do and prepare and if you look through my blog most of the sections have a dedicated post.
Here is the recording:
We got a lot of questions and didn't get to them in the recording so catching up now. If I have missed yours or you think of more then ping me on twitter: @chbatey
Q: When is DSE going to support UDTs?
DSE 4.7 will include a certified version of Cassandra 2.1, sometime in the next few months. Q: Can you alter a UDT? Yes see here: http://www.datastax.com/dev/blog/cql-in-2-1 Q: with denormalized data, how do you handle a store name change or staff name change? First make sure you need the update, when modelling data immutably that is not often the case. If you need to change a small number of rows I'd do it with a small script/program, large number of rows Apache Spark. Q: I had the idea that C* 2.x has vector clock, am I wrong? No Vector clocks in Cassandra, see http://www.datastax.com/dev/blog/why-cassandra-doesnt-need-vector-clocks Q: Using the event source model with frequent rollups, would that not generate a 'queueing' style anti-pattern if data from previous rollup period then gets deleted? If you used the same partition and did range queries, yes. But I would use a partition say per day (or what ever the period is that you didn't have rolled up), thus avoiding ever reading over the deleted data. Q: How would you do the "roll ups" in the account balance calculation example?
Most cases I'd do it in application for the first query that required it. It doesn't matter if two threads get to it first as they can both calculate it and the write to the roll up table would be idempotent. If the rollup calculation takes too long and you don't want to slow down a user request with it then you can schedule it in your app or by a different process.
Q: Why would you not use counters for balance?
Cassandra counters are more for things like statistics, page views etc. You can't update them atomically and they are slower to update then a pure write.
Q: C = Quorum?
Coordinator
Q: How might you go about modeling the "versioning" of time series data so as to avoid updates? I mean where you write a measurement for a particular timestamp and then later on you need to write a new measurement for the same timestamp.
Use a TimeUUID rather than a Timestamp. Then you can have millions per millisecond.
Q: If I perform an "if not exist" write and it fails to reach enough replicas, what state can I expect the data to be in? In other words, can I expect the data to not be written to the cluster?
So assuming it for past the if not exists part (for that you'll get applied = false in the response. Then it is like any write. Cassandra will return how many replicas acked the write. You can't be sure that the rest didn't get it as they may have just not have responded.
Q: I'm wondering if Cassandra could be used to implement distributed locks (Like Redis, Zookeeper)?
You can with LWTs, here are the details: http://www.datastax.com/dev/blog/consensus-on-cassandra
Q: In order to emulate a queue without falling on this anti-pattern, can I use the new Date Time Compaction Strategy and TTL?
Answered at the end of the recording
Q: And we have 24 table per date. After day we create one table on date and drop table per hour. Is it anti patern.
Moving table is like moving partition, it does avoid the anti-pattern but it is a lot of work.
Q: Why not change the tombstome grace period to delete quickly?
You can, but then you need to keep up with repairs which may not be possible.
Q: What would the use case for using Cassandra in a queueing pattern vs. a traditional message oriented middleware?
People typically try and use Cassandra as as queue when they already have it in their infrastructure and they need to get messages from one DC to another. This is when they fall into the anti-pattern.
Q: For the Queue anti-pattern, the > timeuid clause will help on fetch, what about compaction/jvm issues; any recommendations or comments?
Nothing specifically, the best discussion of Cassandra JVM tuning for GC that I have read is here: https://issues.apache.org/jira/browse/CASSANDRA-8150
Q: There are times where data simply cannot be written simultaneously and therefore must be joined at a later time. What do you recommend for joining needs? An external tool such as Spark SQL or ?
Answered at the end of the recording.
Q: Probably one of the best Webinars. Example, were really great. Appreciate DataStax arranging for this. Thanks.
Okay okay this wasn't a question :)
Q: Will quorum reads of a partially-successful counter update get the latest info?
Depends on the number of replicas the write for to and at what consistency. You'll get back in the WriteTimeoutException how many acked the write. If it is a QURORUM (e.g 2 if RF = 3) then it will read it, otherwise you don't know.
Q: Can you point to a good read for retry, no rollback?
On failure modes: http://www.datastax.com/dev/blog/cassandra-error-handling-done-right
Q: How would I go about solving limit offset queries, without having to skip rows programmatically, for example taking a simple page 2 customer table?
Just make sure you have a clustering column and start the next limit query from the last result from the previous query.
Q: You said Cassandra does not do a rollback. Is that true for all cases -- are there any instance where Cassandra would do a rollback?
Not as far as I know.
Q: I missed the beginning. Are UNLOGGED batches OK to use to speed up writes?
See: http://christopher-batey.blogspot.com/2015/02/cassandra-anti-pattern-misuse-of.html
Q: Great presentation. Regarding the secondary index question, the second one should be much more faster, as it hits the primary key, yes?
Yes, so it only needs to go to a small section of the secondary index table as it knows which node the partition is on.
Q: which is the best pattern for timeseries
This depends on the type of time series, quantity/frequency. What you basically want is partitions that don't grow too large, so in the millions, not hundreds of millions and the use of a TimeUUID as the clustering column.
Q: Are the batch execution started in separate threads when using the the batch optimization?
They will be sent off in parallel, I don't know the threading model here but I imagine they are split on one thread and sent aync. A good question for the cassandra devs who hangout in #cassandra on freenode.
Q: What approach can be taken with dse, which is C* 2.0 and doesn't have UDT's?
You can just have a lot of columns! The next DSE version will be 2.1
Q: Using a time bucket is a way to also prevent the rows from growing too wide (I.e. many millions of columns). Any guidance for the recommended tradeoffs between wide rows with slice queries and more narrow rows and some multi-partition queries?
There is rarely a general rule for Cassandra, it is all about your data set and read/write frequency. However in general I do my best to keep all reads from a single partition and go out of my way to keep it at most 2. If you have a very high ingest rate and you read for long periods this can get hard and you may need to go to more partitions.
Q: Do the same rules apply to batch loading when using SSTableLoader and/or the BulkOutputFormat with Hadoop?
I've never used the BulkOutputFormat with Hadoop. For the SSTableLoader. For the sstableloader command, once you have generated the SS tables then it handles the importing.
Q: is BatchType.LOGGED the default for a BatchStatement?
Yes
Q: do we have any ORM framworks for datastax cassandra
The DataStax Java/C# driver now have it built in, there is also the less popular SpringData
Q: What if you have constraint to write data in table only if it is different (by different I meant different by all properties which can be 5-10)?
If you want to write this at a high throughput then I would resolve it at read time as otherwise you'll be doing a read then write which has a lot of race conditions and it a lot slower. IF you include a TimeUUID and write all updates you can then work it out at read time.
Q: Do tombstones get created with data inserted with a TTL and automatically deleted when expired?
Yes it generated a tombstone. For immutable timeseries data the new DateTieredCompaction strategy makes deleting this data a lot more efficient.
Q: Can you go explain a bit more about the de-normalization solution to secondary indexes.
Write the same data but with a partition key as staff ID and the time as the clustering column. This means you can go to a single partition and do a range query. Even a secondary index with a partition key in the query is worse than this as it has to go to the secondary index table and then do a multi partition query in the original table keyed by customer id.
Q: Does the removal of a secondary index cause a performance hit during the delete? Assuming you aren't using the index for any queries
Don't know about this one, I've asked around and will update once i get an answer.
Q: Question about secondary indexes vs inverted indexes...is inverted superior to secondary? Will global indexes replace inverted indexes?
By inverted I am assuming you mean manually inserting data twice with a different primary key. This will always out perform secondary index as you're storing all the customer events for a staff member on one node and sequentially on disk. For global indexes we'll have to wait and see but that is the idea. The only concern I have is you can specialise the double write to exactly what you want (e.g bucket up staff members or not) where as global indexes will have to be a more general solution.
Q: Using the default token split on adding a node in 1.2.x, what issues/symptoms will I experience if I continue to use this method with low numbers of nodes? I assume you're talking about vnodes as without them you pick the token split. The allocation of tokens with vnodes is well discussed here: https://issues.apache.org/jira/browse/CASSANDRA-7032
I work across a desktop and two laptops so reproducible builds mean a lot to me! I often slate Gradle for being buggy and not doing the simple things well (e.g. dependency management for local development).
However it is awesome when you want a quick bit of build logic. I wanted to build my schema for a Cassandra application I am working on to keep my various machines up to date.
So easy in an extensible system like Gradle. I already had my schema creation commands in src/main/resources/schema/tables.cql
I then added a built script dependency to my build.gradle:
Then added a few imports and a couple of nifty tasks:
Of course this relies on one CQL command per line and isn't exactly liquabase but not bad for 10 minutes hacking.
Lots of these hacks can lead to very ugly build scripts so be careful :)
If you're going down the microservice rabbit whole using frameworks like Spring Boot and Dropwizard it is imperative you can monitor what is going on, part of that is pushing metrics to some type of metrics system.
The last set of applications I built used Graphite for this purpose, and fortunately the DataStax Java driver stores lots of interesting metrics using the brilliant dropwizard metrics library.
Here's what it takes to get the Cassandra metrics and your custom metrics from a Spring boot application into Graphite.
This article assumes you know how to use the DataStax Cassandra driver and the Dropwizard metrics library and you're familiar with tools like Maven and Gradle. If you don't go read up on those first.
First let's get the Cassandra driver and metrics libraries on our classapth, here is my example using Gradle:
I've included the Actuator from Spring boot as well.
Assuming you have a bean that is your Cassandra Session add a bean to expose the MetricRegistry and to create a GraphiteReporter:
Here I have a graphite server running on 192.168.10.120. If you don't want to install Graphite to try this out I have a Vagrant VM on my GitHub which launches Graphtie + Graphana.
If we had the Cluster as a bean rather than the Session we'd have injected that. We've now set it up so that all the metrics the DataStax Java driver records will be published to Graphite every 30 seconds.
Now we can plot all kinds of graphs:
For instance we can plot request times, number of errors, number of requests, etc. This becomes even more powerful when you are deploying multiple versions of your application and you pre-fix each instance with a identifier such as its IP.
Adding our own metrics with annotations
The next step is to add more metrics, as the ones in the DataStax library aren't very fine grained, for example we might want to time particular queries, or look at our response times.
You can do this manually but it is easier with annotations. We can do this with the Metric-Spring project. This project integrates Spring AOP with drop wizard metrics.
However it is quite fiddly to get working as we now have three libraries that want to create a MetricRegistry: SpringBoot, Cassandra Driver and Metric-Spring.
To get everyone to use the Cassandra driver's MetricRegistry we need to create a MetricsConfigurerAdapter:
The reason we're injecting the Session is we can no longer register a bean for the MetricRegistry as Spring-Metric does this and we don't want to end up with two. To get this to work we have to remove the metricRegistry bean from the code above. The other thing we do is add the EnableMetric annotation to our Application class:
Once all this is done we can annotate our public methods with @Timed like this:
Then in Graphite we can see them, their name is derived from the fully qualified method name.
So now our Spring Boot application has Cassandra metrics and our own custom application metrics all pushing to Graphite!
The whole application is on GitHub if you want the full Spring config and dependencies.
By far the most common question I get asked when talking about Cassandra is once you've denormalised based on your queries what happens if you were wrong or a new requirement comes in that requires a new type of query.
First I always check that it is a real requirement to be able to have this new functionality on old data. If that's not the case, and often it isn't, then you can just start double/triple writing into the new table.
However if you truly need to have the new functionality on old data then Spark can come to the rescue. The first step is to still double write. We can then backfill using Spark. The awesome thing is that nearly all writes in Cassandra are idempotent, so when we backfill we don't need to worry about inserting data that was already inserted via the new write process.
Let's see an example. Suppose you were storing customer events so you know what they are up to. At first you want to query by customer/time so you end up with following table:
Then the requirement comes in to be able to look for events by staff member. My reaction a couple of years ago would have been something like this:
However if you have Spark workers on each of your Cassandra nodes then this is not an issue.
Assuming you want to a new table keyed by staff_id and have modified your application to double write you do the back fill with Spark. Here's the new table:
Then open up a Spark-shell (or submit a job) with the Spark-Cassandra connector on the classpath and all you'll need is something like this:
How can a few lines do so much! If you're in a shell obviously you don't even need to create a SparkContext. What will happen here is the Spark workers will process the partitions on a Cassandra node that owns the data for the customer table (original table) and insert it back into Cassandra locally. Cassandra will then handle the replication to the correct nodes for the staff table.
This is the least network traffic you could hope to achieve. Any solution that you write your self with Java/Python/Shell will involve pulling the data back to your application and pushing it to a new node, which will then need to replicate it for the new table.
You won't want to do this at a peak time as this will HAMMER you Cassandra cluster as Spark is going to do this quickly. If you have a small DC for just running the Spark jobs and let it asynchronously replicate to your operational DC this is less of a concern.
I've previously blogged about other anti-patterns:
Distributed joins
Unlogged batches
This post is similar to the unlogged batches post but is instead about logged batches.
We'll again go through an example Java application.
The good news is that the common misuse is virtually the same as the last article on unlogged batches, so you know what not to do. The bad news is if you do happen to misuse them it is even worse!
Let's see why. Logged batches are used to ensure that all the statements will eventually succeed. Cassandra achieves this by first writing all the statements to a batch log. That batch log is replicated to two other nodes in case the coordinator fails. If the coordinator fails then another replica for the batch log will take over.
Now that sounds like a lot of work. So if you try to use logged batches as a performance improvement then you'll be very disappointed! For a logged batch with 8 insert statements (equally distributed) in a 8 node cluster it will look something like this:
The coordinator has to do a lot more work than any other node in the cluster. Where if we were to just do them as regular inserts we'd be looking like this:
A nice even workload.
So when would you want to use logged batches?
Short answer: consistent denormalisation. In most cases you won't want to use them, they are a performance hit. However for some tables where you have denormalised you can decide to make sure that both statements succeed. Lets go back to our customer event table from the previous post but also add a customer events by staff id table:
We could insert into this table in a logged batch to ensure that we don't end up with events in one table and not the other. The code for this would look like this:
This would mean both inserts would end up in the batch log and be guaranteed to eventually succeed.
The downside is this adds more work and complexity to our write operations. Logged batches have two opportunities to fail:
When writing to the batch log
When applying the actual statements
Let's forget about reads as they aren't destructive and concentrate on writes. If the first phase fails Cassandra returns a WriteTimeoutException with write type of BATCH_LOG. This you'll need to retry if you want your inserts to take place.
If the second phase fails you'll get a WriteTimeoutException with the write type of BATCH. This means it made it to the batch log so that they will get replayed eventually. If you definitely need to read the writes you would read at SERIAL, meaning any committed batches would be replayed first.
Conclusion
Logged batches are rarely to be used, they add complexity if you try to read at SERIAL after failure and they are a performance hit. If you are going to use them it is in the odd situation where you can't handle inconsistencies between tables. They allow you to guarantee the updates will eventually happen, they do not however offer isolation i.e a client can see part of the batch before it is finished.
I've just put together a simple Spring boot application that has REST endpoints secured by basic auth with the users stored in Cassandra. I want the application to be completely stateless and will assume access is over HTTPS.
I found it surprisingly difficult to plug all this together with Java config, there are very few complete examples so I ended up spending more time looking at the Spring source than I expected. Ah well that just confirms my love of using open source libraries and frameworks.
Essentially you need an extension of the WebSecurityConfigurerAdapterclass where you can programatically add your own UserDetailsService.
Here's my example, I'll explain it below.
Line 11: I've injected the MD5PasswordEncoder as I also use in the code that handles the creation of users in the database.
Line 14-22: Here is where we configure our custom UserDetailsService which I'll show later. We don't want to store user's passwords directly so we use the built in MD5PasswordEncoder. Just using a one way hash isn't good enough as people can break this with reverse lookup tables so we also want to sprinkle in some salt. Our implementation of the UserDetailsService will have a field called Salt and we use the ReflectiveSaltSource to pick it up. Given how common salting passwords is I was surprised there wasn't a separate interface where this was explicit, but ah well.
Line 25-34: Here we define what type of security we want, we tell Spring security to be stateless so it doesn't try and store anything in the container's session store. Then we enable BasicAuth and define the URLs we want to be authorised. The API for creating users is not authorised for obvious reasons.
Next we want to build an implementation of the UserDetailsService interface that checks Cassandra.
I won't go through the Cassandra code in the blog but just assume we have a DAO with the following interface:
If you're interested in the Cassandra code then checkout the while project from GitHub.
With that interface our UserDetailsService looks like this:
Here we use the awesome Optional + Lambda to throw if the user doesn't exist. Our DAO interface doesn't use Runtime exceptions as I like type systems, but this is a nice pattern to convert between a Optional and a library expecting exceptions.
The UserWithSalt is an extension of the Spring's User, with one extra field that the ReflectiveSaltSource will pick up for salting passwords.
That's pretty much it, when a request comes in Spring security will check if the path is authorised, if it is it will get the user details from our UserDetailsService and check the password my using the ReflectiveSaltSource and MD5PasswordEncoder. So our database only has the MD5 password and the salt used to generate it. The salt is self is generated using the Java SecureRandom when users are created.
Full source code is at GitHub and I've created the branch blog-spring-security in case you're reading this in the future and it has all changed!
