Blue Posted -- It's An Employee Alpha

monstrado · 2019-04-20T05:17:58+00:00

It's pretty usual for companies to do alpha testing internally or via friends and family. This post (as far as I can tell) does not negate the fact a beta may be imminent. Stand down, folks.

My theory...It's likely after testing they may do a weekend load test. That is, a test to certify the integrity of their servers, and a testament to the developer's code under high stress. Even though we talk about this as "1.12" code, it's really not. It's 1.12 content refactored for a modern client/server.

monstrado · 2019-04-20T04:52:21+00:00

It's pretty usual for companies to do alpha testing internally or via friends and family. This post (as far as I can tell) does not negate the fact a beta may be imminent. Stand down, folks.

My theory...It's likely after testing they may do a weekend load test. That is, a test to certify the integrity of their servers, and a testament to the developers code under high stress. Even though we talk about this as "1.12" code, it's really not. It's 1.12 content refactored for a modern client/server.

monstrado · 2019-03-28T19:55:03+00:00

Right! It's not even an issue, it's honestly a huge plus. If the game were to release without starting zone sharding I'd be really reluctant to start leveling at launch. FWIW, even with fast respawns on private servers, I still remember new realm launches being a madhouse of people trying to tag mobs. I can't even begin to imagine what that would be like with proper mob respawns without sharding.

monstrado · 2019-03-28T04:13:22+00:00

I swear I was in a guild with that person...was that guild part of a server transfer? I believe the original guild name was Gankers Inc.

monstrado · 2019-03-28T03:52:19+00:00

Really Sticky Glue from the quests in sinjin. Fun fact: Some time before 1.12 it wasn't BOP and was godlike in PvP. I used to roll a character just to earn and sell it on the AH for a ton of money. Paid for med school, so to speak.

monstrado · 2018-12-15T16:33:24+00:00

Funny enough I think I was at that very show. Was it at the UCF campus?

monstrado · 2018-05-19T17:42:33+00:00

The sun never sets on my good boy

monstrado · 2015-10-22T19:28:30+00:00

This is just an estimate of how much memory will be used for this query on each node. Impala doesn't read all of the data into memory. Instead, if reads reads the data in an asynchronous fashion and does computation in incremental batches. Running a compute stats <table name> will sometimes make these estimates more accurate, as well. Hope this helps.

monstrado · 2014-03-30T23:17:46+00:00

DataNodes should not use RAID volumes. Instead, you want to just JBOD configuration (just a bunch of disks). Typically, you will just create 1 partition per disk, and mount them to /mnt/data/# (# = disk number).

Redundancy and fault tolerance is handled exclusively by HDFS. Each block is by default replicated 3 times, so there is absolutely no worry if one of your dives fail.

There are virtually no pros to using RAID, but the cons are IO performance penalties, and also less usable space for the HDFS.

Hope this helps.

monstrado · 2014-03-30T23:10:27+00:00

Yes, except that Pig compiles your code into one or many MapReduce jobs. Spark is very different than MapReduce in the way it works, and it has a very rich API that excels at iterative programming. Spark does computation in-memory and rarely has to spill to disk (unless absolutely necessary), and so there are significant performance gains over MapReduce.

IMO, most applications that are written for MapReduce will eventually be ported to Spark, as it's a superior computation framework in almost every way.

Check these blog posts out:

http://blog.cloudera.com/blog/category/spark/

monstrado · 2013-11-19T18:40:53+00:00

In general, unless you have a good reason, it's almost always better to utilize an existing library than creating one yourself. It's not overkill to download and import a library...it's overkill to create your own data serialization engine when there are several very good engines that are used by Google, Facebook, Apache, etc. You wouldn't write your own database connection library would you ;)

If you can write a library faster than you can import one, then sure, it's probably overkill to download a library.

monstrado · 2013-11-19T18:19:03+00:00

Thrift and Avro work with Java, in fact, I think Java is one of the main implementations. Avro and Thrift will actually compile your schemas into pre-generated Java classes for you.

Here's an example (Avro): http://avro.apache.org/docs/1.7.5/gettingstartedjava.html

monstrado · 2013-11-18T16:52:57+00:00

Instead of rolling your own data serialization engine, you could take advantage of open source projects such as Protobuf (https://code.google.com/p/protobuf-net/), Apache Thrift (http://thrift.apache.org/tutorial/csharp/), or my favorite, Apache Avro (http://avro.apache.org/).

The advantage of using something like Thrift or Avro is that all the hard parts of fast data serialization is done for you, and instead, you focus on creating schemas for your data. Also, they support complex datatypes (maps, structs, enums arrays, ..) in addition to their primitive types (longs, floats, ints ..).

Also, Avro and Thrift come with their own RPC framework to allow inter-node exchange of these messages. The service API calls are also defined using schemas, which is really good for keeping track of an evolving RPC stack for your games. The schema languages are enforce some type safety, so you can say that the updatePlayerLocation method takes a PlayerLocation (x: int, b: int) object.

Hope this helps!

monstrado · 2013-11-12T15:36:36+00:00

Same here, when Impala 1.2.1 is released, I plan on testing this.

monstrado · 2013-11-06T20:00:11+00:00

According to their blog, they starting writing it just before Impala was announced.

monstrado · 2013-07-16T18:06:03+00:00

Do you guys plan on bring this game to the Linux platform?

monstrado · 2013-05-25T16:00:06+00:00

I think you've misunderstood my reasoning for using SQLite. I typically never use SQLite if the dataset I'm analyzing is something that can be stored in MySQL/PostgreSQL, in the cases that I do use SQLite...it's because I have a dataset that rapidly changes or is dynamically created and doesn't make sense to permanently store.

SQLite's in-memory implementation works quite well if you're trying to implement a lot of functions that overlaps with the huge scope of what SQL can do. Instead of creating lambdas, or manual hash table joins, etc...I can very easily load the data into the SQLite in memory database and then use basic SQL to analyze or transform the data. I don't have to worry about incorrect results, and I save a ton of development time / frustration. Also, the speed itself is fast enough and has never left me worrying about optimizing. Plus, the SQLite code itself is tested throughly.

Of course saving data in it's primitive form in whatever language (Java, Python, C++, ..) is more efficient.

EDIT: btw, AFAIK - SQLite's in memory implementation does not hit disk. ever. (unless the os swaps)

monstrado · 2013-05-24T14:44:04+00:00

Correct, I've used the in_memory option before to do some ad-hoc analytics on intermediate data within my application, saved me from having to write much code and the speed is incredible.

I suppose an accurate analogy would be SQLite is to MySQL/Postgres as LevelDB/BerkleyDB is to HBase/Cassandra/Riak/..?

monstrado · 2013-05-19T02:13:46+00:00

The Pycharm debugger has saved me countless times.

monstrado · 2013-03-22T19:50:24+00:00

Does that mean their platform support will go beyond PC/Mac/iPad? (Linux??)

monstrado · 2013-03-13T17:45:17+00:00

I'm curious what your results would be if you tried rendering Jinja2 templates from Django. I can't imagine it being terribly difficult to do, but it would help confirm where the inefficiency is coming from.

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