Over the last few months while working on the prototype around Deconstructed I’ve been using the Orchestrate service offering exclusively. With their service around key value and graph store easily accessible via API it was a no brainer to get started building ASAP. Today, that service goes full beta! You can get the full lowdown at the Orchestrate site.
The basic premise is Orchestrate provides full-text search, time ordered events, graph, key value storage and a lot more. All of these capabilities are offered via an API that create a product that’s extremely easy to get started. Think about what you’d need to do to get full-text search against a key value setup. Really think about it. Yeah? That’s a lot of steps. With Orchestrate you just sign up and start using it. Think about setting up a graph store and managing it on production systems. Yeah? Lot’s of work once it gets used. Again, just sign up, it’s all there, the graph to the key value to the event series and more. All the NoSQL juice you need located in a single service so you’re not fighting and maintaining multiple databases, nodes or whatever you’re working with.
Sing up. Use.
I will copy one thing from the press release….
Ad hoc search queries with Lucene
Event and time-ordered storage for activity feeds, sensor data
Create and query graph relationships
Easy to understand pricing
Data export at will – no lock-in
Standards compliant data security protocols
Daily data backups
Bulk data loading
Daily and hourly usage monitoring
A single, simple interface – JSON data in/out
Designed to complement existing databases and MBaaS services
Client libraries for Java, Node.js, and Go. More on the way!
Using Orchestrate
There are quotes in the press release, but I’ve got a few myself. I’m working to build out a prototype service that I and Aaron Gray will be releasing soon. Our startup is called Deconstructed, but more on that later. Without Orchestrate my dev cycle would be longer each day, as I battle with maintaining the data sources that I need. Without it I would have spent another 2-3 weeks setting up and staging nosql database technology. All things I didn’t really need to do. I needed to focus on the service, the value that we’ll soon bring to our customers.
It really boils down to this, and don’t get me wrong, I’m a total data nerd. But when it comes to building a product or service, the last thing I want to do is fight with managing the data anymore than I have to. That notion inspired me to write “Sorry Database Nerds, Nobody Actually Gives a Shit” which still holds true. I can’t think of a single business that wants to sit around and grok how an index works in a key value or what the spline of text-search queries is going to be.
Pricing
Pricing is sweet, for many that want to try it out things are free. Prices go up a bit more from there, but if you fall into the pricing you’re doing some business and ought to be rolling in a few bucks eh!
The interesting thing to me about pricing is that they’ve structured it around MOp, which stands for MegaOps. More specifically that’s one million API calls or one million operations.
Summary
If you write code, even a little or if you manage data you should do yourself the service and check out what Orchestrate has built. It’s a solid investment of time. I’ll have a lot more on Orchestrate and how we’re using the service for Deconstructed and more on using the service with JavaScript in the coming months. Keep your eyes peeled and I might even have some Dart and C# magic thrown in there to boot! Check em’ out, until later, happy hacking.
NOTE: If you just want to check out the code bits, scroll down to the sub-title #symphonize #hacking. Also important to note I’m putting the library through a fairly big refactor at the moment so that everything aligns with the documentation that I’ve recently created. So many things may not be implemented, but we’re moving toward v0.1.0, which will be a functional implementation of the library available via npm based entirely on the documentation and specs that I outline after the history.
There are two main reasons why I chose Orchestrate.io and a data generation library as the two things I wanted to combine. The first, is I knew the orchestrate.io team and really dug what they were building. I wanted to work with it and check out how well it would work for my use cases in the future. The ability to go sit down, discuss with them what they were building was great (which I interviewed Matt Heitzenroder @roder that you can watch Orchestrate.io, Stop Dealing With the Database Infrastructure!) The second reason is that my own startup that I’m co-founding with Aaron Gray (@agray) needed to use key value and graph data storage of some type, somewhere. Orchestrate.io looked like a perfect fit. After some research, giving it a go, it fit very well into what we are building.
December then rolled into the standard holiday doldrums and slowdowns. So fast forward to January post a few rounds of beer and good tidings and I got the 3rd in the series published titled Getting Serious With Symphony.js – JavaScript TDD/BDD Coding Practices (3/3). The post doesn’t speak too much to symphony.js usage but instead my efforts to use TDD or BDD practices in trying to write the library.
Slowly I made progress in building the library and finally it’s in a mostly releasable state now. I use this library daily in working with the code base for Deconstructed and imagine I’ll use it ongoing for many other projects. I hope others might be able to find uses for it too and maybe even add capabilities or ideas. Just ping me via Twitter @adron or Github @adron, add an issue on Github and I’ll be happy to accept pull requests for new features, code refactoring, add you to the project or whatever else you’re interested in.
#symphonize #hacking
Now for the nitty gritty. If you’re up for using or contributing to the project check out the symphonize.js github pages site first. It’s got all the information to help get you kick started. However, you can keep reading as I’ve included much of the information there along with the examples from the README.md below.
NOTE: As I mentioned at the top of this blog entry, the funcitonal implementation of code isn’t available via npm just yet, myself and some others are ripping through a good refactor to align the implementation fo the library with the rewritten and newly available documentation – included blow and at the github pages.
[sourcecode language=”javascript”]
git clone git@github.com:YourUserName/symphonize.git
cd symphonize
npm install
[/sourcecode]
Using The Library
The intended usage is to invocate the JavaScript object and then call generate. That’s it, a super simple process. The code would look like this:
[sourcecode language=”javascript”]var Symphonize = require(‘../bin/symphonize’);
var symphonize = new Symphonize();
[/sourcecode]
The basic constructor invocation like this utilizes the generate.json file to generate data from. To inject the json configuration programmatically just inject the json configuration information via the constructor.
[sourcecode language=”javascript”]
var configJson = {"schema":"keyvalue"};
var Symphonize = require(‘../bin/symphonize’);
var symphonize = new Symphonize();
[/sourcecode]
Once the Symphonize data generator has been created call the generate() method as shown.
That’s basically it. But you say, it’s supposed to do X, Y or Z. Well that’s where the json configuration data comes into play. In the configuration data you can set the data fields and what they’ll generate, what type of data will be generated, the specific schema, how many records to create and more.
generate.json
The library comes with the generate.json file already setup with a working example. Currently the generation file looks like this:
[sourcecode language=”javascript”]
{
"schema": "keyvalue", /* keyvalue, graph, event, geo */
"count": 20, /* X values to generate. */
"write_source": "console", /* console, orchestrateio and whatever other data sources that might come up. */
"fields": {
/* generates a random name. */
"fieldName": "name",
/* generates a random dice roll of a d20. */
"fieldTwo": "d20",
/* A single lorum ipsum random statement is genereated. */
"fieldSentence": "sentence",
/* A random guid is generated. */
"fieldGuid": "guid" }
}
[/sourcecode]
Configuration File Definitions
Each of the configuration options that are available have a default in the configuration file. The default is listed in italics with each definition of the configuration option listed below.
“schema” : This is used to select what type of data structure type is going to be generated. The default iskeyvalue for this option.
“count” : This provides the total records that are to be generated by the library. The default is 1 for this option.
“write_source” : This provides the location to output the generated data to. The default is console for this option.
“fields” : This is a JSON field within the JSON configuration file that provides configuration options around the fields, number of fields and their respective data to generate. The default is one field, with a default data type of guid. Each of the respective entries in this JSON option is a self contained JSON name and value pair. This then looks simply like this (which is also shown above in part):[sourcecode language=”javascript”]{
"someBoolean": "boolean",
"someChar": "character",
"aFloat": "float",
"GetAnInt": "integer",
"fieldTwo": "d20",
"diceRollD10": "d10",
"_string": {
"fieldName": "NameOfFieldForString",
"length": 5,
"pool": "abcdefgh"
},
"_sentence": {
"fieldName": "NameOfFiledOfSentences",
"sentence": "5"
},
"fieldGuid": "guid"
}
[/sourcecode]
Fields Configuration: For each of the fields you can either set the field to a particular data type or leave it empty. If the field name and value pair is left empty then the field defaults to guid. The types of data to generate for fields are listed below. These listed are all simple field and data generation types. More complex nested generation types are listed below under Complex Field Configuration below the simple section.
“boolean“: This generates a boolean value of true or false.
“character“: This generates a single character, such as ‘1’, ‘g’ or ‘N’.
“float“: This generates a float value, similar to something like -211920142886.5024.
“integer“: This generates an integer value, similar to something like 1, 14 or 24032.
“d4“: This generates a random integer value based on a dice roll of one four sided dice. The integer range being 1-10.
“d6“: This generates a random integer value based on a dice roll of one six sided dice. The integer range being 1-10.
“d8“: This generates a random integer value based on a dice roll of one eight sided dice. The integer range being 1-10.
“d10“: This generates a random integer value based on a dice roll of one ten sided dice. The integer range being 1-10.
“d12“: This generates a random integer value based on a dice roll of one twelve sided dice. The integer range being 1-10.
“d20“: This generates a random integer value based on a dice roll of one twenty sided dice. The integer range being 1-20.
“d30“: This generates a random integer value based on a dice roll of one thirty sided dice. The integer range being 1-10.
“d100“: This generates a random integer value based on a dice roll of one hundred sided dice. The integer range being 1-10.
“guid“: This generates a random globally unique identifier. This value would be similar to ‘F0D8368D-85E2-54FB-73C4-2D60374295E3’, ‘e0aa6c0d-0af3-485d-b31a-21db00922517’ or ‘1627f683-efeb-4db8-8174-a5f2e3378c87’.
Complex Field Configuration: Some fields require more complex configuration for data generation, simply because the data needs some baseline of what the range or length of the values need to be. The following list details each of these. It is also important to note that these complex field configurations do not have defaults, each value must be set in the JSON configuration or an error will be thrown detailing that a complex field type wasn’t designated. Each of these complex field types is a JSON name and value parameter. The name is the passed in data type with a preceding underscore ‘_’ to generate with the value having the configuration parameters for that particular data type.
“_string“: This generates string data based on a length and pool parameters. Required fields for this include fieldName, length and pool. The JSON would look like this:[sourcecode language=”javascript”]"_string": {
"fieldName": "NameOfFieldForString",
"length": 5,
"pool": "abcdefgh"
}
[/sourcecode]
Samples of the result would look like this for the field; ‘abdef’, ‘hgcde’ or ‘ahdfg’.
“_hash“: This generates a hash based on the length and upper parameters. Required fields for this included fieldName, length and upper. The JSON would look like this:[sourcecode language=”javascript”]"_hash": {
"fieldName": "HashFieldName",
"length": 25,
"casing": ‘upper’
}
[/sourcecode]
Samples of the result would look like this for the field: ‘e5162f27da96ed8e1ae51def1ba643b91d2581d8’ or ‘3F2EB3FB85D88984C1EC4F46A3DBE740B5E0E56E’.
“_name”: This generates a name based on the middle, *middleinitial* and prefix parameters. Required fields for this included fieldName, middle, middle_initial and prefix. The JSON would look like this:[sourcecode language=”javascript”]"_name": {
"fieldName": "nameFieldName",
"middle": true,
"middle_initial": true,
"prefix": true
}
[/sourcecode]
Samples of the result would look like this for the field: ‘Dafi Vatemi’, ‘Nelgatwu Powuku Heup’, ‘Ezme I Iza’, ‘Doctor Suosat Am’, ‘Mrs. Suosat Am’ or ‘Mr. Suosat Am’.
So that covers the kick start of how eventually you’ll be able to setup, use and generate data. Until then, jump into the project and give us a hand.
So I’ve been in more than a few conversations about data structures, various academic conversations and other notions about where and how data should be stored. I’ve been on projects and managed projects that involve teams of people determining how to manage data so that other people can just not manage data. They want to focus on business use and not the data mechanisms underneath. The root of everything around databases really boils down to a single thing – how can we store X and retrieve X – nobody actually trying to get business done or change the world is going to dig into the data storage mechanisms if they don’t have to. To summarize,
nobody actually gives a shit…
At least nobody does until the database breaks, or somebody has to be hired to manage or tune queries or something or some other problem comes up. In the ideal world we could just put data into the ether and have it come back when we ask for it. Unfortunately we have to keep caring for where the data is, how it’s stored, the schema (even in schema-less, you still need to know the schema of the data at some point, it’s just another abstraction to push off dealing with the database), how to backup, recover, data gravity, proximity and a host of other concerns. Wouldn’t it be cool if we could just work on our app or business? Wouldn’t it be nice to just, well, focus on things we actually give a shit about?
Managed Data Systems!
The whole *aaS and PaaS World has been pushing to simplify operations to the point that the primary, if not the only concern, is the business itself. This is a pretty big step in many ways, but holds a lot of hope and promise around fixing the data gravity, proximity, management and related concerns. One provider of services that has an interesting start around the NoSQL realm is Orchestrate.io. I’ll have more about them in the future, as I’ll actually be working on hacking on some code against their platform. They’re currently solving a number of the mentioned issues. Which is great, a solid starting point that takes us past the draconian nature of the old approach to NoSQL and Relational Databases in general.
There has been some others, such as Mongo Labs or such, that have created a sort of DBaaS. This however doesn’t fill the gap that Orchestrate.io is filling. So far almost every *aaS database or other solution has merely been a single type of database that a developer can just throw data at in a single kind of way. Not really flexible, and really only abstracting some manual work, but not providing much additional value add around using the actual data. Orchestrate.io is bridging these together with search, replication and other features to provide a platform on which multiple options are available via the API. Key value, geo, time series and others are all coming together for them nicely. Having all the options actually creates a real value add, versus just provide one single way to do one thing.
Intelligent Data Systems?
After checking out and interviewing Orchestrate.io recently I’ve stumbled into a few other ideas. It would be perfect for them to implement or for the open source community to take a stab at. What would happen if the systems storing the data knew where to put things? What would be the case for providing an intelligent indexing policy or architecture at the schema design decision layer, the area where a person usually must intervene? Could it be done?
A decision tier that scans and makes decisions on the data to revamp the way it is stored against a key value, geo, time series or other method. Could it be done in real time? Would it have to go through some type of processing system? The options around implementing something like this are numerous, but this just leaves a lot of space for providing value add around the data to reduce the complexity of this decision making.
Imagine you have key value data, that needs to be associative based on graph principles, that you must store in a highly available system with pertinent real-time data provided based on those graph relations. A decision layer, to create an intelligent data system, could monitor the data and determine the frequent query paths against the data. If the data is growing old it could move data from real-time to archival via the key value. Other decisions could be made to push up data segments into a cache tier or some other mechanism to provide realtime graph connections to client queries. These are all decisions that would need to be made by somebody working on the data, but could be put into a set of rules to allow for re-allocation of the data via automated mechanisms into better storage options. Why keep old data that isn’t queried in the active in memory graph store, push it to the distributed key store. Why keep the graph data on drive when it can be in memory with correlated keys in a key value in memory store, backed by an on drive key value? All valid decisions, all becoming better understood day by day. It’s about time some of this decision process started to be automated.
What are your thoughts? Pro-intelligent data systems or anti-intelligent data systems? Think it’ll work or is it the wrong approach? Maybe the system should approach some other zenith or axiom point to become truly abstracted and transparent?
Today I’m starting a project working with Orchestrate.io’s API & open source software collaborations. More about the project in a moment, let’s get up to speed on what I’ll be including in this project. My main focus is to build a client library to access Orchestrate.io. During building this I’ll dive into the key value, graph and other storage mechanisms that the client library will provide. Beyond that, I’ll take a stroll through building an NPM library and the pertinent JavaScript the library. So buckle up, we’re going on a code slinging hash writing hacking session.
Over the course of putting together this material, I’ll be posting most of the core material on Orchestrate.io’s blog, so subscribe for updates as they come out. Feedly is a good option, connect via searching for “orchestrate.io” or navigate over to the Orchestrate.io blog itself. 😉
Project Effort Context
During building the client I’ll take a dive into who, what, where, when, why and how to interact with the various data structures. I’ll aim for the client to follow the model of the existing Go Client Library that is available at Orchestrate Go Client on Github. It follows a basic model as shown below in Go language.
[sourcecode language=”cpp”]
c := client.NewClient("Your API Key")
// Get a value
value, _ := c.Get("collection", "key")
// Put a value
c.Put("collection", "key", strings.NewReader("Some JSON"))
// Search
results, _ := c.Search("collection", "A Lucene Query")
// Get Events
events, _ := c.GetEvents("collection", "key", "kind")
// Put Event
c.PutEvent("collection", "key", "kind", strings.NewReader("Some JSON"))
// Get Relations
relations, _ := c.GetRelations("collection", "key", []string{"kind", "kind"})
// Put Relation
c.PutRelation("sourceCollection", "sourceKey", "kind", "sinkCollection", "sinkKey")
[/sourcecode]
I’ll be working on this client, but don’t hold back on me, feel free to jump in with some of your own code or telling me I wrote some code wrong or whatever. I’d gladly accept any committers jumping in to help out. The more we all work together the more useful information I can provide during this project.
Once this project has produced a workable client pending interest from the community I’ll put together some material about where, how and some best uses around using the client in your Node.js Application. Even prospectively build a JavaScript client side library prospectively for use with Angular or other popular client side libraries.
Full context – Riak 1.3 RC came out just a couple dozen hours ago. RC stands for release candidate, which in turn basically means that version 1.3 is complete and any other additions will be for quick fixes or any issues that crop up. I’ve just started rolling a few new systems myself with this new version and hope you’ll join me in taking a hack at it. Let’s jump into a few reason why you’d want to leap into 1.3. You can read about the features below via the release notes also, but I’ve turned them into smaller bit size chunks below.
Giddyup in action!
The first thing with the latest v1.3 has been the massive effort put into testing via the riak_test and the giddyup repos. Ongoing there will be a much easier way to move forward in features & quality. This is one of the reasons I love working for Basho, the whole team isn’t about smoke and mirrors with testing, they readily and diligently work on testing. Which to add context, remember we’re talking about distributed systems here, which aren’t exactly the easiest thing to test. One doesn’t just merely walk in and write unit tests and assume a distributed systems is tested. This moves us forward, and those that want to contribute and get involved more heavily in Riak now have a platform to dive in confidently when using these testing repositories.
Active Anti-Entropy – Alright, now we’re getting to the features with bad ass sounding names. Also referred to as AAE, this feature grabs bad replica data and begins a correction through read repair to protect data. It’s one more layer of protection against any type of data loss, disaster, bit rot, etc).
MapReduce Sink Backpressure – This one reminds me of tuning when setting up forced induction, AKA a turbo on a car. But I digress, I’ve snagged a description from the release notes for this feature, “Riak Pipe brought inter-stage backpressure to Riak KV’s MapReduce system. However, prior to Riak 1.3, that backpressure did not extend to the sink. It was assumed that the Protocol Buffers or HTTP endpoint could handle the full output rate of the pipe. With Riak 1.3, backpressure has been extended to the sink so that those endpoint processes no longer become overwhelmed. This backpressure is tunable via a soft cap on the size of the sink’s buffer, and a period at which a worker should check that cap. These can be configured at the Riak console by setting application environment variables” ….suffice it to say this helps out with map reduce in certain situations.
Additional IPv6 Support – Riak Handoff and Protocol Buggers listen ala IPv6 now. Nuff’ said.
Luke removal – Luke is completely and utterly gone now. Dead. Don’t look for Luke here.
Riaknostic – This is now part of the default featureset instead of separate tooling.
SmartOS 1.8 Packages – They’re available.
Health Check – This is a pretty awesome system that’s been added. Basically it watches the system and enables and disables services based on conditions. It’s super easy, just flick the switch in the app.config.
[sourcecode language=”yml”]
{enable_health_checks, true}
[/sourcecode]
Reset Bucket Properties – A quickie definition from the release notes “The HTTP interface now supports resetting bucket properties to their default values. Bucket properties are stored in Riak’s ring structure that is gossiped around the cluster. Resetting bucket properties for buckets that are no longer used or that are using the default properties can reduce the amount of gossiped data.”
There were also a lot of PRs and more that you can check out on Github. These are the main key features that are now available and ready for use in 1.3. Check em’ out, feel free to contact me or any of the team to ask questions, let us know your 2 cents or otherwise banter about. Cheers! Sometime in the coming days I’ll have a quick start, akin to what’s in the docs, but with some specific ops on some IaaS Providers. So keep reading, coming up soon.
At least nobody does until the database breaks, or somebody has to be hired to manage or tune queries or something or some other problem comes up. In the ideal world we could just put data into the ether and have it come back when we ask for it. Unfortunately we have to keep caring for where the data is, how it’s stored, the schema (even in schema-less, you still need to know the schema of the data at some point, it’s just another abstraction to push off dealing with the database), how to backup, recover, 
Managed Data Systems!
Intelligent Data Systems?
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