A simple explanation of Rings in Riak? - erlang

I'm trying to understand what the "rings" in Riak are, but I can't seem to find a clear explanation (please don't just link me to a web site, I have already read what is on the web). As far as I understand a node in Riak is a partition in a ring. Is that correct?

I know you've said you read everything on the web already, but for others also reading this question, I'd like to post two resources that discuss Riak's "ring":
http://riak.basho.com/arch.html
http://riak.basho.com/edoc/architecture.txt
With that out of the way, Riak uses the word "ring" in two places. The first is to describe the hash space that is used for determining where to store data. The reason Riak calls that space a ring is that the last value in the space (2^160-1) is thought of as being adjacent to the first value in the space (0). Replicas of data are stored in the "next N partitions" of the hash space, following the partition to which the key hashes. Considering the hash space as a ring gives a convenient definition for the "next parition after the final partition."
The other use of the word "ring" is related to, but not exactly the same as the former. I mentioned partitions: each node claims several segments of the hash space, called partitions. Knowledge about which node has claimed which partition is stored in a structure that Riak calls the "ring state", or sometimes just the "ring." Other cluster metadata may also be kept in the ring state, because it's a conveniently shared piece of data throughout the cluster.
In general usage, an application shouldn't need to think about the ring much.
Does that answer your question? I'd encourage you to post questions like this to the riak-users mailing list ( http://lists.basho.com/mailman/listinfo/riak-users_lists.basho.com ). I'm sure other members of that list are interested in these answers, and we (the Riak developers) tend to be more attentive to that feed than this one.

Related

replicating trees between ACID RDB using CRDT

I'm interested in replicating "hierachies" of data say similar to addresses.
Area
District
Sector
Unit
but you may have different pieces of data associated to each layer, so you may know the area of Sectors, but not of units, and you may know the population of a unit, basically its not a homogenious tree.
I know little about replication of data except brushing Brewers theorem/CAP, and some naive intuition about what eventual consistency is.
I'm looking for SIMPLE mechanisms to replicate this data from an ACID RDB, into other ACID RDBs, systemically the system needs to eventually converge, and obviously each RDB will enforce its own local consistent view, but any 2 nodes may not match at any given time (except 'eventually').
The simplest way to approach this is to simple store all the data in a single message from some designated leader and distribute it...like an overnight dump and load process, but thats too big.
So the next simplest thing (I thought) was if something inside an area changes, I can export the complete set of data inside an area, and load it into the nodes, thats still quite a coarse algorithm.
The next step was if, say an 'object' at any level changed, was to send all the data in the path to that 'object', i.e. if something in a sector is amended, you would send the data associated to the sector, its parent the district, and its parent the sector (with some sort of version stamp and lets say last update wins)....what i wanted to do was to ensure that any replication 'update' was guaranteed to succeed (so it needs the whole path, which potentially would be created if it didn't exist).
then i stumbled on CRDTs and thought....ah...I'm reinventing the wheel here, and the algorithms are allegedly easy in principle, but tricky to get correct in practice
are there standards accepted patterns to do this sort of thing?
In my use case the hierarchies are quite shallow, and there is only a single designated leader (at this time), I'm quite attracted to state based CRDTs because then I can ignore ordering.
Simplicity is the key requirement.
Actually it appears I've reinvented (in a very crude naive way) the SHELF algorithm.
I'll write some code and see if I can get it to work, and try to understand whats going on.

What indices does jena TDB2 use?

I am trying to find out what indices TDB2 builds. I found out by the code that it uses B+ trees to store them on disc but I didn't get what they contain and how they are used.
So my detailed questions are:
For which collation order of RDF triples (like SPO, SOP, POS, PSO, ... ) does it build indices?
How are RDF Terms encoded and stored?
What strategy is used to load the indices into main memory? (I would expect paging)?
It would also help me if you could point me to a white paper or something similar about TDB2's software design. I searched for it but couldn't find anything.
TDB2 has a "id" for each RDF term (literal's URIs, blank nodes). The id is a fixed length 64. Another way of say ting is it keeps a dictionary.
For triples it keeps SPO, POS, and OSP (this is configurable but that's the default). A triple is stored in an index as those ids - so 3 ids per triple. Fixed length.
Indexes are memory mapped files outside the heap by default. They provide the good usability.
That's the current default setup. The code isolates changes e.g. 64 bit ids could be longer, different index choices made.

Beam/Dataflow design pattern to enrich documents based on database queries

Evaluating Dataflow, and am trying to figure out if/how to do the following.
My apologies if anything in the above is trivial--trying to wrap our heads around Dataflow before we make a decision on using Beam, or something else like Spark, etc.
General use case is for machine learning:
Ingesting documents which are individually processed.
In addition to easy-to-write transforms, we'd like to enrich each document based on queries against databases (that are largely key-value stores).
A simple example would be a gazetteer: decompose the text into ngrams, and then check if those ngrams reside in some database, and record (within a transformed version of the original doc) the entity identifier given phrases map to.
How to do this efficiently?
NAIVE (although possibly tricky with the serialization requirement?):
Each document could simply query the database individually (similar to Querying a relational database through Google DataFlow Transformer), but, given that most of these are simple key-value stores, it seems like there should be a more efficient way to do this (given the real problems with database query latency).
SCENARIO #1: Improved?:
Current strawman is to store the tables in Bigquery, pull them down (https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/gcp/bigquery.py), and then use them as side inputs, that are used as key-value lookups within the per-doc function(s).
Key-value tables range from generally very small to not-huge (100s of MBs, maybe low GBs). Multiple CoGroupByKey with same key apache beam ("Side inputs can be arbitrarily large - there is no limit; we have seen pipelines successfully run using side inputs of 1+TB in size") suggests this is reasonable, at least from a size POV.
1) Does this make sense? Is this the "correct" design pattern for this scenario?
2) If this is a good design pattern...how do I actually implement this?
https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/gcp/bigquery.py#L53 shows feeding the result to the document function as an AsList.
i) Presumably, AsDict is more appropriate here, for the above use case? So I'd probably need to run some transformations first on the Bigquery output to separate it into key, value tuple; and make sure that the keys are unique; and then use it as a side input.
ii) Then I need to use the side input in the function.
What I'm not clear on:
for both of these, how to manipulate the output coming off of the Bigquery pull is murky to me. How would I accomplish (i) (assuming it is necessary)? Meaning, what does the data format look like (raw bytes? strings? is there a good example I can look into?)
Similarly, if AsDict is the correct way to pass it into the func, can I just reference things like a dict normally is used in python? e.g., side_input.get('blah') ?
SCENARIO #2: Even more improved? (for specific cases):
The above scenario--if achievable--definitely does seem like it is superior continuous remote calls (given the simple key-value lookup), and would be very helpful for some of our scenarios. But if I take a scenario like a gazetteer lookup (like above)...is there an even more optimized solution?
Something like, for every doc, writing our all the ngrams as keys, with values as the underlying indices (docid+indices within the doc), and then doing some sort of join between these ngrams and the phrases in our gazeteer...and then doing another set of transforms to recover the original docs (now w/ their new annotations).
I.e., let Beam handle all of the joins/lookups directly?
Theoretical advantage is that Beam may be a lot quicker in doing this than, for each doc, looping over all of the ngrams and doing a check if the ngram is in the side_input.
Other key issues:
3) If this is a good way to do things, is there any trick to making this work well in the streaming scenario? Text elsewhere suggests that the side input caching works more poorly outside the batch scenario. Right now, we're focused on batch, but streaming will become relevant in serving live predictions.
4) Any Beam-related reason to prefer Java>Python for any of the above? We've got a good amount of existing Python code to move to Dataflow, so would heavily prefer Python...but not sure if there are any hidden issues with Python in the above (e.g., I've noticed Python doesn't support certain features or I/O).
EDIT: Strawman? for the example ngram lookup scenario (should generalize strongly to general K:V lookup)
Phrases = get from bigquery
Docs (indexed by docid) (direct input from text or protobufs, e.g.)
Transform: phrases -> (phrase, entity) tuples
Transform: docs -> ngrams (phrase, docid, coordinates [in document])
CoGroupByKey key=phrase: (phrase, entity, docid, coords)
CoGroupByKey key=docid, group((phrase, entity, docid, coords), Docs)
Then we can iteratively finalize each doc, using the set of (phrase, entity, docid, coords) and each Doc
Regarding the scenarios for your pipeline:
Naive scenario
You are right that per-element querying of a database is undesirable.
If your key-value store is able to support low-latency lookups by reusing an open connection, you can define a global connection that is initialized once per worker instead of once per bundle. This should be acceptable your k-v store supports efficient lookups over existing connections.
Improved scenario
If that's not feasible, then BQ is a great way to keep and pull in your data.
You can definitely use AsDict side inputs, and simply go side_input[my_key] or side_input.get(my_key).
Your pipeline could look something like so:
kv_query = "SELECT key, value FROM my:table.name"
p = beam.Pipeline()
documents_pcoll = p | ReadDocuments()
additional_data_pcoll = (p
| beam.io.BigQuerySource(query=kv_query)
# Make row a key-value tuple.
| 'format bq' >> beam.Map(lambda row: (row['key'], row['value'])))
enriched_docs = (documents_pcoll
| 'join' >> beam.Map(lambda doc, query: enrich_doc(doc, query[doc['key']]),
query=AsDict(additional_data_pcoll)))
Unfortunately, this has one shortcoming, and that's the fact that Python does not currently support arbitrarily large side inputs (it currently loads all of the K-V into a single Python dictionary). If your side-input data is large, then you'll want to avoid this option.
Note This will change in the future, but we can't be sure ATM.
Further Improved
Another way of joining two datasets is to use CoGroupByKey. The loading of documents, and of K-V additional data should not change, but when joining, you'd do something like so:
# Turn the documents into key-value tuples as well[
documents_kv_pcoll = (documents_pcoll
| 'format docs' >> beam.Map(lambda doc: (doc['key'], doc)))
enriched_docs = ({'docs': documents_kv_pcoll, 'additional_data': additional_data_pcoll}
| beam.CoGroupByKey()
| 'enrich' >> beam.Map(lambda x: enrich_doc(x['docs'][0], x['additional_data'][0]))
CoGroupByKey will allow you to use arbitrarily large collections on either side.
Answering your questions
You can see an example of using BigQuery as a side input in the cookbook. As you can see there, the data comes parsed (I believe that it comes in their original data types, but it may come in string/unicode). Check the docs (or feel free to ask) if you need to know more.
Currently, Python streaming is in alpha, and it does not support side inputs; but it does support shuffle features such as CoGroupByKey. Your pipeline using CoGroupByKey should work well in streaming.
A reason to prefer Java over Python is that all these features work in Java (unlimited-size side inputs, streaming side inputs). But it seems that for your use case, Python may have all you need.
Note: The code snippets are approximate, but you should be able to debug them using the DirectRunner.
Feel free to ask for clarification, or to ask about other aspects if you feel like it'd help.

NeuroEvolution of Augmenting Topologies (NEAT) and global innovation number

I was not able to find why we should have a global innovation number for every new connection gene in NEAT.
From my little knowledge of NEAT, every innovation number corresponds directly with an node_in, node_out pair, so, why not only use this pair of ids instead of the innovation number? Which new information there is in this innovation number? chronology?
Update
Is it an algorithm optimization?
Note: this more of an extended comment than an answer.
You encountered a problem I also just encountered whilst developing a NEAT version for javascript. The original paper published in ~2002 is very unclear.
The original paper contains the following:
Whenever a new
gene appears (through structural mutation), a global innovation number is incremented
and assigned to that gene. The innovation numbers thus represent a chronology of the
appearance of every gene in the system. [..] ; innovation numbers are never changed. Thus, the historical origin of every
gene in the system is known throughout evolution.
But the paper is very unclear about the following case, say we have two ; 'identical' (same structure) networks:
The networks above were initial networks; the networks have the same innovation ID, namely [0, 1]. So now the networks randomly mutate an extra connection.
Boom! By chance, they mutated to the same new structure. However, the connection ID's are completely different, namely [0, 2, 3] for parent1 and [0, 4, 5] for parent2 as the ID is globally counted.
But the NEAT algorithm fails to determine that these structures are the same. When one of the parents scores higher than the other, it's not a problem. But when the parents have the same fitness, we have a problem.
Because the paper states:
In composing the offspring, genes are randomly chosen from veither parent at matching genes, whereas all excess or disjoint genes are always included from the more fit parent, or if they are equally fit, from both parents.
So if the parents are equally fit, the offspring will have connections [0, 2, 3, 4, 5]. Which means that some nodes have double connections... Removing global innovation counters, and just assign id's by looking at node_in and node_out, you avoid this problem.
So when you have equally fit parents, yes you have optimized the algorithm. But this is almost never the case.
Quite interesting: in the newer version of the paper, they actually removed that bolded line! Older version here.
By the way, you can solve this problem by instead of assigning innovation ID's, assign ID based on node_in and node_out using pairing functions. This creates quite interesting neural networks when fitness is equal:
I can't provide a detailed answer, but the innovation number enables certain functionality within the NEAT model to be optimal (like calculating the species of a gene), as well as allowing crossover between the variable length genomes. Crossover is not necessary in NEAT, but it can be done, due to the innovation number.
I got all my answers from here:
http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf
It's a good read
During crossover, we have to consider two genomes that share a connection between the two same nodes in their personal neural networks. How do we detect this collision without iterating both genome's connection genes over and over again for each step of crossover? Easy: if both connections being examined during crossover share an innovation number, they are connecting the same two nodes because they received that connection from the same common ancestor.
Easy Example:
If I am a genome with a specific connection gene with innovation number 'i', my children that take gene 'i' from me may eventually cross over with each other in 100 generations. We have to detect when these two evolved versions (alleles) of my gene 'i' are in collision to prevent taking both. Taking two of the same gene would cause the phenotype to probably loop and crash, killing the genotype.
When I created my first implementation of NEAT I thought the same... why would you keep a innovation number tracker...? and why would you use it only for one generation? Wouldn't be better to not keep it at all and use a key value par with the nodes connected?
Now that I am implementing my third revision I can see what Kenneth Stanley tried to do with them and why he wanted to keep them only for one generation.
When a connection is created, it will start its optimization in that moment. It marks its origin. If the same connection pops out in another generation, that will start its optimization then. Generation numbers try to separate the ones which come from a common ancestor, so the ones that have been optimized for many generations are not put side to side that one that was just generated. If a same connection is found in two genomes, that means that that gene comes from the same origin and thus, can be aligned.
Imagine then that you have your generation champion. Some of their genes will have 50 percent chance to be lost due that the aligned genes are treated equally.
What is better...? I haven't seen any experiments comparing the two approaches.
Kenneth Stanley also addressed this issue in the NEAT users page: https://www.cs.ucf.edu/~kstanley/neat.html
Should a record of innovations be kept around forever, or only for the current
generation?
In my implementation of NEAT, the record is only kept for a generation, but there
is nothing wrong with keeping them around forever. In fact, it may work better.
Here is the long explanation:
The reason I didn't keep the record around for the entire run in my
implementation of NEAT was because I felt that calling something the same
mutation that happened under completely different circumstances was not
intuitive. That is, it is likely that several generations down the line, the
"meaning" or contribution of the same connection relative to all the other
connections in a network is different than it would have been if it had appeared
generations ago. I used a single generation as a yardstick for this kind of
situation, although that is admittedly ad hoc.
That said, functionally speaking, I don't think there is anything wrong with
keeping innovations around forever. The main effect is to generate fewer species.
Conversely, not keeping them around leads to more species..some of them
representing the same thing but separated nonetheless. It is not currently clear
which method produces better results under what circumstances.
Note that as species diverge, calling a connection that appeared in one species a
different name than one that appeared earlier in another just increases the
incompatibility of the species. This doesn't change things much since they were
incompatible to begin with. On the other hand, if the same species adds a
connection that it added in an earlier generation, that must mean some members of
the species had not adopted that connection yet...so now it is likely that the
first "version" of that connection that starts being helpful will win out, and
the other will die away. The third case is where a connection has already been
generally adopted by a species. In that case, there can be no mutation creating
the same connection in that species since it is already taken. The main point is,
you don't really expect too many truly similar structures with different markings
to emerge, even with only keeping the record around for 1 generation.
Which way works best is a good question. If you have any interesting experimental
results on this question, please let me know.
My third revision will allow both options. I will add more information to this answer when I have results about it.

How does "DHT search engine" work?

I'm interested in the Btdigg.org which is called a "DHT search engine". According to this article, it doesn't store any content and even has no database. Then how does it work? Doesn't it need to gather meta infos and store them in database like other normal search engines? After a user submit a query, it scans the DHT network and return the results in "real time"? Is this possible?
I don't have specific insight into BTDigg, but I believe the claim that there is not database (or something that acts like a database) is a false statement. The author of that article might have been referring to something more specific that you might encounter in a traditional torrent site, where actual .torrent files are stored for instance.
This is how a BTDigg-like site works:
You run a bunch of DHT nodes, specifically with the purpose of "eaves dropping" on DHT traffic, to be introduced to info-hashes that people talk about.
join those swarms and download the metadata (.torrent file) by using the ut_metadata extension
index the information you find in there, map it to the info-hash
Provide a front-end for that index
If you want to luxury it up a bit you can also periodically scrape the info-hashes you know about to gather stats over time and maybe also figure out when swarms die out and should be removed from the index.
So, the claim that you don't store .torrent files nor any content is true.
It is not realistic to search the DHT in real-time, because the DHT is not organized around keyword searches, you need to build and maintain the index continuously, "in the background".
EDIT:
Since this answer, an optimization (BEP 51) has been implemented in some DHT clients that lets you query which info-hashes they are hosting, significantly reducing the cost of indexing.
For a deep understanding of DHT and its applications, see Scott Wolchok's paper and presentation "Crawling BitTorrent DHTs for Fun and Profit". He presents the autonomous search engine idea as a sidenote to his study of DHT's security:
PDF of his paper:
https://www.usenix.org/legacy/event/woot10/tech/full_papers/Wolchok.pdf
His presentation at DEFCON 18 (parts 1 & 2)
http://www.youtube.com/watch?v=v4Q_F4XmNEc
http://www.youtube.com/watch?v=mO3DfLtKPGs
https://www.usenix.org/legacy/event/woot10/tech/full_papers/Wolchok.pdf
The method used in Section 3 seems to suggest a database to store all the torrent data is required. While performance is better, it may not be a true DHT search engine.
Section 8, while less efficient, seems to be a DHT search engine as long as the keywords are the store values.
From Section 3, Bootstrapping Bittorent Search:
"The system handles user queries by treating the
concatenation of each torrent's filenames and description as a
document in the typical information retrieval model and using an
inverted index to match keywords to torrents. This has the advantage
of being well supported by popular open-source relational DBMSs. We
rank the search results according to the popularity of the torrent,
which we can infer from the number of peers listed in the DHT"
From Section 8, Related Work:
the usual approach to distributing search using a DHT is
with an inverted index, by storing each (keyword, list of matching
documents) pair as a key-value pair in the DHT. Joung et al. [17]
describe this approach and point out its performance problems: the
Zipf distribution of keywords among files results in very skewed load
balance, document information is replicated once for each keyword in
the document, and it is difficult to rank documents in a distributed
environment
It is divided into two steps.
To achieve bep_0005 protocol got infohash, you do not need to implement all protocol requires only now find_node (request), get_peers (response), announce_peer (response). Here's one of my open source dhtspider.
To achieve bep_0009 protocol got metainfo index it, here are my own a bittorrent search engine, every day can get unique infohash 300w +, effective metainfo 50w +.

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