sharding

问题 I am new to Cassandra was going though this Article explaining sharding and replication and I am stuck at a point that is - I have a cluster with 6 Cassandra nodes configured at my local machine. I create a new keyspace "TestKeySpace" with replication factor as 6 and a table in keyspace "employee" and primary key is auto-increment-number named RID. I am not able to understand how this data will be partitioned and replicated. What I want to know is since I am keeping my replication factor to

问题 I know there is a ShardingTest() object that can be used to create a testing sharding environment (see https://serverfault.com/questions/590576/installing-multiple-mongodb-versions-on-the-same-server), eg: mongo --nodb cluster = new ShardingTest({shards : 3, rs : false}) However, given that the disk space in my testing machine is limited and I'm getting "Insufficient free space for journal files" errors when using the above command, I'd like to set the smallfiles option. I have tried with the

问题 I am working on a Java application that requires horizontal partitioning of data in different PostgreSQL servers. I would like to use a JPA framework and Spring for transaction management. The most popular frameworks for sharding data with JPA seem to be Hibernate Shards, which appears to be no longer in development, and OpenJPA Slice, which does not support virtual shards (one of my requirements). Are there any other options that I'm missing, or a way to get around the OpenJPA limitation?

问题 Short version If I split my users into shards, how do I offer a "user search"? Obviously, I don't want every search to hit every shard. Long version By shard, I mean have multiple databases where each contains a fraction of the total data. For (a naive) example, the databases UserA, UserB, etc. might contain users whose names begin with "A", "B", etc. When a new user signs up, I simple examine his name and put him into the correct database. When a returning user signs in, I again look at his

问题 I've read about sharding a collection in MongoDB. MongoDB lets me shard a collection explicitly by calling shardCollection method. There I can choose whether I want it to be rangely shareded or hashingly sharded. My question is, what would happen if I didn't call the shardCollection method, and I had say 100 nodes? Would MongoDB keep the collections intact and distribute them across the cluster? Would MongoDB keep all the collections in a single node? Do I completely not understand how this

问题 $lookup is new in MongoDB 3.2. It performs a left outer join to an unsharded collection in the same database to filter in documents from the “joined” collection for processing. To use $lookup , the from collection cannot be sharded. On the other hand, sharding is a useful horizontal scaling approach. What's the best practise to use them together? 回答1: As the docs you quote indicate, you can't use $lookup on a sharded collection. So the best practice workaround is to perform the lookup

问题 I am looking for a good design pattern for sharding a list in Google App Engine. I have read about and implemented sharded counters as described in the Google Docs here but I am now trying to apply the same principle to a list. Below is my problem and possible solution - please can I get your input? Problem: A user on my system could receive many messages kind of like a online chat system. I'd like the server to record all incoming messages (they will contain several fields - from, to, etc).

问题 I'm trying to create an replicated sharded cluster in mongodb. Initially I've created two shards and there are a replica set with three members in each shard. And all the shards and replicasets run in a single machine. I followed http://docs.mongodb.org/manual/tutorial/convert-replica-set-to-replicated-shard-cluster/ to deploy this structure and that worked perfectly. But as I'm running my mongodb in an AWS instance for a business application and I'm connecting my node.js server with the

问题 The situation: I have a set of 12 tables (representing data by month) that are sharded across 6 databases. I need to get a sample set of data across any of these databases for any given month. Why I used Classical Mappping Model rather than Declarative Model: I only require access to 1 of the 12 types of table as I will only be gathering a sample of data for a single given month each time this code is run. The Classical Mapping Model allows me to dynamically define the table name I want to

问题 For testing purposes, I have configured a 4-node cluster, each of them has a Spark Worker and a MongoDB Shard. These are the details: Four Debian 9 servers (named visa0, visa1, visa2, visa3) Spark(v2.4.0) cluster on 4 nodes (visa1: master, visa0..3: slaves) MongoDB (v3.2.11) sharded cluster con 4 nodes ( config server replica set on visa1..3, mongos on visa1, shard servers: visa0..3 ) I'm using MongoDB Spark connector installed with "spark-shell --packages org.mongodb.spark:mongo-spark