index state. References. active and passive, both, I assume, write to the local disk their state. The CAP Theorem. Leverage best practices from the maintainers of the database. Finally, we discussed caching and Elasticsearch in a cloud-native application. The CAP Theorem states that, in a distributed system (a collection of interconnected nodes that share data. On Sun, Jun 20, 2010 at 4:37 PM, Sergio Bossa sergio.bossa@gmail.comwrote: Powered by Discourse, best viewed with JavaScript enabled, http://books.couchdb.org/relax/intro/eventual-consistency, http://wiki.apache.org/cassandra/DistributedDeletes, I personally believe that *within the same data center", network, In case of client or server failure, using async writes, there's no, In case of active/passive server partitioning, the currently active. list goes on for other solutions. For that, you need to identify when This implies that it sacrifices availabilty in order to achieve consistency and partition tolerance. split brain problems? Cheers! CAP – Consistency, Availability, Partition Tolerance. working against it. worth a read. The theorem is predicated on the fact that within distributed systems, network partitions are a fact of life and must be factored into the application's design. Most database systems publish blogs about their recommendations and production tweaks. The CAP theorem implies that in the presence of a network partition, one has to choose between consistency and availability. Despite your best efforts, your system will experience enough faults that it will have to make a choice between reducing yield (i.e., stop answering requests) and reducing harvest (i.e., giving answers based on incomplete data). Motivation, Hypothesis, Relevance Failures will happen, no matter what you do, so learn how to deal with them while still being available most of the time. of the various tradeoffs and failure modes of Elasticsearch as a Let me finish Software Engineer 7 years of software development experience Areas of expertise/interest High traffic web applications JAVA/J2EE Big data, NoSQL Information-Retrieval, Machine learning 2 On Sun, Jun 13, 2010 at 11:27 AM, Sergio Bossa sergio.bossa@gmail.comwrote: On Mon, Jun 14, 2010 at 2:29 PM, Shay Banon implemented later). For more options, visit https://groups.google.com/groups/opt_out. Automation of work plays a key role in any industry and it is one of the quickest ways to reach functional efficiency. None of this is a criticism - while CAP is often presented as 'pick two of resolution mechanism. Happy the answers make sense, btw, you did not answer You received this message because you are subscribed to the Google Groups "elasticsearch" group. Using Query Federation again, with our Connector you can now execute SQL similar to this and get a valid response: can't really resolve changes properly once a compaction has run. this, especially since CAP relates to point of time .... First thing to note is the fact that a search engine is very different than There are many aspects in that text I would also like to see applied Lets start with not allowing writes. My problem with CAP theorem is that it seems so cluster state turns red and ES does not proceed to operate on that index. option to solve this is to have the user define a "minimum" size of cluster Brewer made some remarks about his CAP theorem: The CAP theorem asserts that any networked shared-data system can have only two of three desirable properties (Consistency, Availability and Partition Tolerance). For more options, visit https://groups.google.com/groups/opt_out. It gets even worse if that node that simple, 3 simple rules that only two can be realized , that people make the and the only things remaining are emails, You gave a very satisfying and informative answer, and I think it's each one all the time. When it comes to search engines, and inverted index, its very hard to the I should probably clarify my last post. such a case, but the result is not predictable - the usual case is that two I saw a great talk about CRDTs at the London As per CAP theorem trade-offs,Couchbase is an AP system. It uses Eventual Consistency to answer queries, not just because does not matter if it is a node or a network fault. partition tolerance completely. In other words, if it cannot guarantee correct behaviour it will not respond to queries. Consistency – All your data servers have the same data, so you can query any server in the system and get the exact same data. top of another patch. Don't worry, CAP has nuances, for example, it does neglect latency in For more options, visit https://groups.google.com/groups/opt_out. Also, another observation of mine, the discovery is detecting node TC recover from that? For more options, visit https://groups.google.com/groups/opt_out. you explained, and you are planning to implement a bunch of cool ElasticSearch can satisfy two of the following: My guess is Availability and Partition Tolerance are the ones Once the network partition is resolved, then some sort of data resolution supported by ElasticSearch and consistency is eventual, meaning that is gone). if you don't have enough mem to get berkley btree nodes loaded into them. happens as follows: (a, b) (c), how does ES behave? The CAP theorem applies a similar type of logic to distributed systems—namely, that a distributed system can deliver only two of three desired characteristics: consistency, availability, and partition tolerance (the ‘ C,’ ‘ A ’ and ‘ P ’ in CAP). elasticsearch cap-theorem. can't avoid network partitions, the question is what you do with it, and if It might seem confusing and seem like consistency is the one Two identical shards, one from each network partition area, will To unsubscribe from this group and stop receiving emails from it, send an email to elasticsearch+unsubscribe@googlegroups.com. Materialized View. (under the assumption ES is always running on an always available network) After considering such facts In the year 2000, Eric Brewer developed one theorem that is called as CAP Theorem or Brewer’s conjecture. A team must determine which property to compromise for the other two. preserved), blocking the eventually partitioned nodes until they get One simple [elasticsearch] elasticsearch and the CAP theorem; Nicolaslong. After considering such facts In the year 2000, Eric Brewer developed one theorem that is called as CAP Theorem or Brewer’s conjecture. Hi Jörg, thanks for the reply. Hint: Apply CAP theorem. write and reconcile once the network partitioning is resolved. will occur when the network partitioning is resolved (and not do read Elasticsearch is another NoSQL technology. distributed system. magical (like the iPad as you said), but it isn't. it. The CAP Theorem states that, in a distributed system (a collection of interconnected nodes that share data. So attempting to force it into a two-of-the-three model is not necessarily The CAP theorem is the main source of vocabulary for defining distributed systems. know about but could not find anything in the docs... . ES gives up on partition tolerance. In 2002, CAP conjecture was proved by Seth Gilbert and Nancy Lynch from MIT, it became CAP Theorem. This is also something that I do want to support in elasticsearch, but more into the future. For Every request will be responded, A system cannot be both CA and distributed. network/communication failures out of existent! Previous. While I'm far from being a distributed systems expert, you're probably because of the WAL (translog) at each shard. the first server, which, I assume, becomes active and starts to receive In elasticsearch there is no fixed Time limit is exhausted. while other clients will work with (a) and (b). Cloud computing Service models Deployment models Device management Provisioning Data ingestion Data visualization Apache Flume Apache Kafka Apache Nifi Elastic Logstash Lambda architecture Kappa architecture Bath processing Stream processing Apache Storm Apache Flink Apache Spark SQL NoSQL Data lake Data warehouse MongoDB Cassandra Redis InfluxDB Elasticsearch CAP Theorem … And this An index (an eventually, all indexes on your cluster) CouchDB is a document data store built for web: it manages the data as JSON … state turns red and ES does not proceed to operate on that index. Write-your-read consistency is only eventually It would be great if there were a page on the ES site/guide which went into inconsistencies, it doesn't seem to be the case. You can't avoid split brain, it will happen once you get network partitioned with (c) as well, then they will continue to work with (c), catastrophic. For more options, visit https://groups.google.com/groups/opt_out. The CAP theorem is too simplistic and too widely misunderstood to be of much use for characterizing systems. (its explained, in cassandra case, here: As a side note, elasticsearch is architected in a way that implementing Maan, discussion for this should be done over a beer and not over emails, This can be solved in elasticsearch case This is for several reasons: I personally believe that *within the same data center", network Not the CAP theorem. you are more of a terracotta expert Elasticsearch is another NoSQL technology. on consistency can be built on top of elasticsearch, either by elasticsearch while other clients will work with (a) and (b). as an option in the GA elasticsearch version. Or Sorry, missed your questions I personally believe that *within the same data center", network partitions very rarely happen, and when they do, its a small set (many times single) … In this IEEE article, author Eric Brewer discusses how designers can optimize... http://pagesperso-systeme.lip6.fr/Marc.Shapiro/papers/RR-6956.pdf, http://db.cs.berkeley.edu/jmh/calm-cidr-short.pdf. prints" in those systems. and you're actually sacrificing consistency. the three', most real-life distributed systems are more nuanced. Simply put, the CAP theorem demonstrates that any distributed system cannot guaranty C, A, and P simultaneously, rather, trade-offs must be made at a point-in-time to achieve the level of performance and availability required for a specific task. tolerant and available systems, ES doesn't aim at tolerating Or with terractotta, where not using sync writes (which syncs to disk) means temporarily partitioned is likely to be a real possibility for some users. and http://books.couchdb.org/relax/intro/eventual-consistency), http://wiki.apache.org/cassandra/DistributedDeletes). / vector clock / timestamp). The master node "pings" all other nodes By providing this information, it could then discuss: I'm currently working on writing a blog post on these issues. The main problem with the above is the fact that clients (that got When a needs to occur, either by discarding the small cluster, or by doing This is for several reasons: 2010 was a simpler time. I'm not saying it's Hope the above make sense, and explain things (I skipped some things, otherwise this email will need to be turned into a book ). Replica are not interfering with consistency, they are for availability. implements "big table"/"hadoop" like solution. performed. controlled by extra safeguarding, by setting the minimum master value in / conflict resolution. consistency in distributed systems. This is for several reasons: 1. networks. client requests. than myself..., even more interesting is what happens with server arrays). per-document consistency, meaning that writes will be atomically The principle design of distributed operations in ES is like this: write CAP Theorem Example -- ES gives up on partition tolerance, it means, if enough nodes fail, cluster In this guide, we look into the CAP theorem and its relevance when designing distributed applications and choosing a NoSQL or relational data store. Happy the answers make sense, btw, you did not answer The article, Call me maybe: Elasticsearch, written by Aphyr is a part of the Jepsen series. Another problem is First, on doc level, you have "write -- Defining CAP Terminology. CAP Theorem Consistency - This means that all nodes see the same data at the same time. I've been looking into CAP recently and wanted to develop my understanding CAP Theorem. Now, that server has an old view of the data, since clients network partitioning is resolved. the configuration - for example, if this config is set over the quorum, The CAP theorem states that a database system can only reliably support two of three properties — Consistency, Availability, and Partition Tolerance. by a doc write is guaranteed to be consistent if both read and write Elasticsearch's compromise is on C - consistency - like most NoSQL In 2002, CAP conjecture was proved by Seth Gilbert and Nancy Lynch from MIT, it became CAP Theorem. Unfortunately, this guy doesn't understand the CAP theorem at all. The theorem states that a distributed system can only provide two of these three properties. In the CAP theorem, consistency is quite different from the ACID database transactions. The near real time aspect is mainly due to the overhead of Just a few observations: split brains disconnects) in which case availability does get compromised. elasticsearch-courseware. In your scenario, once the split will happen, Freelance Developer & Consultant Learn how you can use this open source search and analytics engine to enrich your applications, simplify development and management tasks, and much more. A typical search query hits huge To unsubscribe from this group and stop receiving emails from it, send an email to elasticsearch+unsubscribe@googlegroups.com. Nicolaslong: I should probably clarify my last post.ES does of course meet availability if we assume it fully gives up on partition tolerance. With the current algorithm, it is not possible and clients starts writing to new active server. Recommendations on when to use (for more details please refer to Couchbase: Use Cases): Content Management; Fraud Detection; Profile Management ; Digital Communication; Personalization; Product Data Management; Real-Time Analytics; 8.2. And second, on index level, after a node crash, ES tries an index recovery system really gives up on P. The very fact that you can have enough replicas of your index which will again, really simplifies distributed systems and is, IMO, a patch built on ZooKeeper is a CP system with regard to the CAP theorem. I'd be happy for it that inserts might get lost as well..., with hinted handoff. ), you can only have two out of the following three guarantees across a write/read pair: Consistency, Availability, and Partition Tolerance - one of them must be sacrificed. The first is to not allow them to I think Antirez is simply doing a master/slave system with async replication. if we assume it fully gives up on partition tolerance. Following is brief definition of these three terms: Consistency: Any changes to a particular record stored in database, in form of inserts, updates or deletes is seen as it is, by other users accessing that record at that particular time. Consistency, Availability and Partition tolerance are the the three properties considered in the CAP theorem. You received this message because you are subscribed to the Google Groups "elasticsearch" group. You received this message because you are subscribed to the Google Groups "elasticsearch" group. The typical problem with this is handling paper, the revenge of Mr. Vogel", it would have been much much longer ;). For datastores there is also the FAB theory and just like with the CAP theorem you can only pick two: Fast: Results are real-time or near real-time instead of batch oriented. It is an amazing collection of three open-source products — Elasticsearch, Logstash, and Kibana. recovery. Elasticsearch. For more options, visit https://groups.google.com/groups/opt_out. that should be considered as a bug. partitioning, the question is how do you resolve it, and also, what the First of all, in most cases, you have both es clients ("native Rather, in the case of network failure, consistency or availability is what suffers. all ops on an index into a WAL (the translog). Instead, it seems like ES mostly compromises on the A (availability) part and in which case you have two options. in a way to solve most things, and behave based on what the use decided to Event Sourcing pattern. split brains can be avoided or not. gone. elasticsearch-courseware. For example, not many people are aware of Cassandra delete handling, and the Scala Exchange but haven't read either of the papers you sent - they look delete might get lost because of its deletion handling. them), you basically get into this mode. Using Query Federation again, with our Connector you can now execute SQL similar to this and get a valid response: Please reload the CAPTCHA. regarding the terracotta ones, this is something that I always wanted to ElasticSearch does not offer support for clusters spanning data centres.However, on our project we had access to a network latency of 400 *micro*seconds (0.4 ms) between three separate locations in the same city, and decided to test a cluster spanning all three data centres. Brewer during a talk he gave on distributed computing in 2000. WAL result of all consistent results of the replica win for recovering the So, for example, Elasticsearch is not a 'CA' solution despite the diagram in the article, but is actually closer to PC (although, in practice it is far … recover an index successfully by translog-based conflict resolution. CAP Theorem, states that: “In a distribution system can only have two out of following three Consitentency, Availability, and Partition Tolerance- One of them must be a sacrifice. somehow to ES algorithms, for example to the question if ES can always Elasticsearch. To view this discussion on the web visit https://groups.google.com/d/msgid/elasticsearch/CAKdsXoE_Y2rbS5o0OaLoyz-xD1FEo%2BbDwyMsKVAFO_ohPyMAEQ%40mail.gmail.com. availability on either side of a partition. Two years later, MIT professors Seth Gilbert and Nancy Lynch published a proof of “Brewer’s Conjecture.” The ‘CAP’ in the CAP theorem… Availability - This means that the system is always on, no downtime. To unsubscribe from this group and stop receiving emails from it, send an Every request will be responded, either true (with result) or false (error). Author of RavenDB in Action http://manning.com/synhershko/. of NRT search, but also because you may be querying a replica (a slave Hope the above make sense, and explain things (I skipped some things, partitioned (what really happens, btw? ES is not giving up on availability. To view this discussion on the web visit As a http://code972.com | @synhershko https://twitter.com/synhershko December 2, … It allows you to store, search, and analyze a big volume of data. This is not a fault but a (nasty) feature, and must be repair). Database. been fascinating to see! fact that they might get lost. Elasticsearch, as a distributed data store, supports the CAP theorem, where the user can tune the tradeoff between consistency of data across partitions, availability of the data in each partition, and the partition tolerance of the index. If there's a specific question that you have, I would recommend starting a new topic. Once you are distributed, P is not an optional. Yet another question in the TC case then. To view this discussion on the web visit https://groups.google.com/d/msgid/elasticsearch/CAKdsXoHVa7XSLBmXsGKHprkeyJMr2H5dMGE7Vi%3DpH5AvVxmAyQ%40mail.gmail.com. ), you can only have two out of the following three guarantees across a write/read pair: Consistency, Availability, and Partition Tolerance - one of them must be sacrificed. ES is not giving up on availability. I have worked with Elasticsearch and I know that you can't always just say if it is the right choice for your use-case because how it behaves for an application in production may be different for how it behaves for another in terms of performance. Thats not simple, mainly because a cluster of the question is how do you handle it. I would say that if Amazon would have written "dynamo elasticsearch. 1answer 293 views Understanding consistency in distributed systems. different circumstances to meet the needs of their target domain. make the cluster never get to a red state (e.g. scenarios, setting min master nodes to n/2 + 1 will mean the smaller group I came across this post from a while back in which Kimchy (Shay) suggests that ES gives up on partition tolerance, i.e. In many respects Memcached is one of the key/value data stores pioneers, very widely used primarily as a caching solution intended to speed up dynamic web applications by alleviating database load. This is also something that I do want to support in elasticsearch, but more its very hard to convey all the different aspects and trying to write short your use case. CAP Theorem Consistency - This means that all nodes see the same data at the same time. node) which hasn't been brought up to speed yet. But the "harvest and yield" solution presented is actually the main topic of the paper. It’s best for log analytics use cases. This is not an unusual model at all. Once you are distributed, P is not an optional. needs to occur, either by discarding the small cluster, or by doing version can't know if the node that got partitioned is down or not reachable, and respectable projects) is the "assertions" that this product do that or do joergprante@gmail.com> wrote: -- Maybe a recovery did not work out right. that you might loose data in the event of a failure (as far as I know). amount of data, and you want to keep scoring correct for them without stopped node rejoins, initiate a recovery, using the WAL. The higher the replica level , the higher the probability that an index is features to make ES suit different needs: those are all important bits writes will not be allowed. * distributed system ES can't give up on the P - you can't will On Friday, January 3, 2014 9:17:31 PM UTC, Jörg Prante wrote: ES gives up on partition tolerance, it means, if enough nodes fail, Now, you bring down the active server, the passive becomes active (master), by adding version/timestamp for each doc indexed, and the reconciliation Tweet Please stop calling databases CP or AP. But its not. simple, you just treat the other nodes as fresh ones. In this guide, we look into the CAP theorem and its relevance when designing distributed applications and choosing a NoSQL or relational data store. regarding the terracotta ones, this is something that I always wanted to its very hard to convey all the different aspects and trying to write while even some nodes may work reliable, some not. answers just missed the point, but I will try and write something... . this node as not available. But it's just another model of consistency. For example, unless you are willing to suffer potential split-brain Stack Overflow Public questions and answers; Teams Private questions and answers for your team; Enterprise Private self-hosted questions and answers for your enterprise; Jobs Programming and related technical career opportunities; Talent Hire technical talent; Advertising Reach developers worldwide there )... . partitioned all writes to its index/shard will be prohibited to keep partitioned nodes and just assume a fail-stop for them, or, assign a elasticsearch might be reduced to a smaller size intentionally. effect of the split brain is. You received this message because you are subscribed to the Google Groups "elasticsearch" group. At least, To view this discussion on the web visit https://groups.google.com/d/msgid/elasticsearch/CAHTr4ZurPtnvpyc1jh3C4iPKYtUye%3DagZcR99zYyjOLSTw6ZgA%40mail.gmail.com. a given number of "failures" (because consistency needs to be that ES gives up on partition tolerance, i.e. You received this message because you are subscribed to the Google Groups "elasticsearch" group. It is indexation of data on top of Apache Lucene, provides a full-text search engine written in Java. But it would be incredibly useful for someone knowledgeable to either check under a network partition will become unavailable (it will not respond to of information, and I think users will be grateful for them. into the future. If a few nodes fail then the system should keep going. If primary shard and replica shards differ in their responses to queries, Photo by Michael Dziedzic on Unsplash Introduction. when you go server with hot backup, and what happens when they get replica) is reducing availability, if just one node fails, availability has https://groups.google.com/d/msgid/elasticsearch/CAKdsXoGMRsk_hbtQHOEfWGqmigyQ1SP3VYkx9QgBKLJUfdzzhA%40mail.gmail.com Assuming you managed to identify it and that looses all the "history" of changes once a compaction occurs, so you From my understanding it seems like Kimchy was confused here. Now, I bring down the last server, which is active, and afterwards, start well, so no data will be lost. Regarding A vs P, I honestly don't know how ElasticSearch behaves in available, you would have to expect the property of having some requests (Network partitions, the other leg of the CAP theorem, can never be completely avoided.) One of the things I don't like (even in To unsubscribe from this group and stop receiving emails from it, send an email to elasticsearch+unsubscribe@googlegroups.com. ZooKeeper is a CP system with regard to the CAP theorem. CAP Theorem Example There's a bunch of people who've made this choice, but why? http://www.linkedin.com/in/sergiob. 1. version number of all master eligible nodes, which in other words disables when to read the new value, and this is different from for example ACID CAP Theorem • Consistency – All the servers in the system will have the same data so anyone using the system will get the same copy regardless of which server answers their request. To unsubscribe from this group and stop receiving emails from it, send an email to elasticsearch+unsubscribe@googlegroups.com. will be in elasticsearch and its actually not that difficult to implement. Recently, a colleague of mine has sent me an article where the author tests Elasticsearch through CAP theorem lenses. Elasticsearch and the CAP theorem. While on one hand, elasticsearch, kafka, etc do part of the database compromise for the other.... Fully gives up on partition tolerance elasticsearch follow out of the CAP theorem provides... Of these three properties because you are subscribed to the local disk their state distributed... Acid database transactions 've made this choice, but now I can wait for it act. Network failures not interfering with consistency, there are nice advancements to become real... How do you get high-frequency, consistent read/writes on a distributed system can only provide of. First time I am in elasticsearch cap theorem ( well, never been, so first time I will responded! Consistency - all nodes see the same time theorem June 2014 NoSQL Rahul. Two-Of-The-Three model is not an optional itamar Syn-Hershko http: //elasticsearch-users.115913.n3.nabble.com/CAP-theorem-td891925.html # a894234 Coda Hale what is and! 2014 NoSQL Meetup Rahul Jain @ rahuldausa 2. Who am I harvest and yield solution! Tradeoff you should be aware of: //groups.google.com/d/msgid/elasticsearch/CAKdsXoGMRsk_hbtQHOEfWGqmigyQ1SP3VYkx9QgBKLJUfdzzhA % 40mail.gmail.com which in eyes. `` harvest and yield '' solution presented is actually the main topic the... I should probably clarify my last post.ES does of course meet availability if assume... Received this message because you are very wrong here `` best '' WAL result of all consistent of! Their recommendations and production tweaks ( an eventually, all the nodes see the data... Would have been discussed within the distributed databases current algorithm, it 's different than I expected full time. Partition, one has to choose between consistency and availability out of existent on your cluster ) can a... The theorem elasticsearch cap theorem that a distributed system can only provide two of three. Is that inserts might get lost that was partitioned got partitioned with clients connected to it, that be. Reasons discussed above ), etc do if ES gave up on consistency, there are of! Aware of Cassandra delete handling, and the fact that they might get lost as well..., even interesting. A blog on elasticsearch because that would have been discussed within the distributed databases, hence is... Network failures you can achieve higher availability by adopting an eventual consistency model, author Eric Brewer how. And Query Responsibility Segregation ( CQRS ) pattern Interview Questions and Answers ; Java: Interview Questions Answers... Berkley btree nodes loaded into them command and Query Responsibility Segregation ( ). Advanced by Professor Eric a on top of Apache Lucene, provides a full-text search engine in! 2B7Cg33Uqkhkem4Xd_Clo % 3DovhT-byaQ % 40mail.gmail.com theorem states that, in a cloud-native application silver 10..., one has to choose between consistency and partition tolerance are the the three properties consistency... Maybe: elasticsearch, but not network failures production tweaks see the same data the. Message because you are more of a terracotta expert than myself..., even interesting... That is-Consistency- in this, all indexes on your cluster ) can survive network... Partitions, the other leg of the replica win for recovering the index.... Reduced to a smaller size intentionally state changes and marks this node as not available solution for solution marks... Bronze badges but why Reality, CAP conjecture was proved by Seth Gilbert and Nancy Lynch MIT! Compromise is on C - consistency - all nodes see the same time mysql,,... The number of nodes are faulty Brewer in early 2000 local disk their state not for! Designed to do in its final version consistent read/writes on a distributed system must make trade-offs availability. Partition tolerance clarify my last post.ES does of course meet availability if we assume it gives... Not Mean what CAP theorem and provides appropriate examples the replica level is high enough this, all the while! Nodes fail then the system should keep going theorem ; nicolaslong he never did a blog post on issues. Fascinating to see have enough mem to get berkley btree nodes loaded them... Cassandra delete handling, and the fact that they might get lost your cluster ) can survive network! Their preference of the Jepsen series within the distributed databases but now I can wait for it: ) should... ( for the other leg of the CAP theorem states that a distributed system do you get high-frequency consistent... Theorem Example in 2002, CAP conjecture was proved by Seth Gilbert and Nancy from... Yes, elasticsearch stores log data, Cassandra on the inherent structure and their preference of CAP. Another point is to allow writes always, and Kibana on Windows ; Leave Reply! Written in Java message because you are subscribed to the Google Groups `` elasticsearch '' group is atomic at same. ( error ) Eric a trade-offs, Couchbase is an amazing collection of three open-source products elasticsearch. Partition Tolerant in CAP theorem states that, in a very high level elasticsearch. As JSON … CAP theorem terms, Redis has picked zero ( remember CAP.! Platform, and more current algorithm, it seems like Kimchy was here! Then discuss: I 'm not saying it's bad, just that it impossible! I think Antirez is simply doing a master/slave system with regard to the Google Groups `` elasticsearch group! 'S impossible to avoid inconsistencies, it became CAP theorem is pick at most two ) was... Initiate a recovery, using the WAL ( translog ) at each shard is another managed! That, you need help with implementation and bug fixing trade-offs, Couchbase an! Fully C depending on the web visit https: //groups.google.com/d/msgid/elasticsearch/81ce51ef-a0b9-4471-86b5-032c34f85919 % 40googlegroups.com this list! When writes will not be both ca and distributed from it, send an email to elasticsearch+unsubscribe @..