OneShard

The OneShard feature offers a practicable solution that enables significantly improved performance and transactional guarantees for cluster deployments

The OneShard option for ArangoDB clusters restricts all collections of a database to a single shard and places them on one DB-Server node. This way, whole queries can be pushed to and executed on that server, massively reducing cluster-internal communication. The Coordinator only gets back the final result.

Queries are always limited to a single database, and with the data of a whole database on a single node, the OneShard option allows running transactions with ACID guarantees on shard leaders.

A OneShard setup is highly recommended for most graph use cases and join-heavy queries.

Without the OneShard feature query processing works as follows in a cluster:

• The Coordinator accepts and analyzes the query.
• If collections are accessed then the Coordinator distributes the accesses to collections to different DB-Servers that hold parts (shards) of the collections in question.
• This distributed access requires network-traffic from Coordinator to DB-Servers and back from DB-Servers to Coordinators and is therefore expensive.

Another cost factor is the memory and CPU time required on the Coordinator when it has to process several concurrent complex queries. In such situations Coordinators may become a bottleneck in query processing, because they need to send and receive data on several connections, build up results for collection accesses from the received parts followed by further processing.

If the database involved in a query is a OneShard database, then the OneShard optimization can be applied to run the query on the responsible DB-Server node like on a single server. However, it still being a cluster setup means collections can be replicated synchronously to ensure resilience etc.

How to use the OneShard feature

The OneShard feature is enabled by default if you use the ArangoDB Enterprise Edition and if the database is sharded as "single". In this case the optimizer rule cluster-one-shard is applied automatically. There are two ways to achieve this:

• If you want your entire cluster to be a OneShard deployment, use the startup option --cluster.force-one-shard. It sets the immutable sharding database property to "single" for all newly created databases, which in turn enforces the OneShard conditions for collections that are created in it. The _graphs system collection is used for distributeShardsLike.

• For individual OneShard databases, set the sharding database property to "single" to enforce the OneShard condition. The _graphs system collection is used for distributeShardsLike. It is not possible to change the sharding database property afterwards or overwrite this setting for individual collections. For non-OneShard databases the value of the sharding database property is either "" or "flexible".

Example

The easiest way to make use of the OneShard feature is to create a database with the extra option { sharding: "single" }. As done in the following example:

arangosh> db._createDatabase("oneShardDB", { sharding: "single" } )

arangosh> db._useDatabase("oneShardDB")

arangosh@oneShardDB> db._properties()
{
  "id" : "6010005",
  "name" : "oneShardDB",
  "isSystem" : false,
  "sharding" : "single",
  "replicationFactor" : 1,
  "writeConcern" : 1,
  "path" : ""
}

Now you can go ahead and create a collection as usual:

arangosh@oneShardDB> db._create("example1")

arangosh@oneShardDB> db.example1.properties()
{
  "isSmart" : false,
  "isSystem" : false,
  "waitForSync" : false,
  "shardKeys" : [
    "_key"
  ],
  "numberOfShards" : 1,
  "keyOptions" : {
    "allowUserKeys" : true,
    "type" : "traditional"
  },
  "replicationFactor" : 2,
  "minReplicationFactor" : 1,
  "writeConcern" : 1,
  "distributeShardsLike" : "_graphs",
  "shardingStrategy" : "hash",
  "cacheEnabled" : false
}

As you can see, the numberOfShards is set to 1 and distributeShardsLike is set to _graphs. These attributes have automatically been set because the { "sharding": "single" } options object was specified when creating the database.

To do this manually for individual collections, use { "sharding": "flexible" } on the database level and then create a collection in the following way:

db._create("example2", { "numberOfShards": 1 , "distributeShardsLike": "_graphs" })

Here, the _graphs collection is used again, but any other existing collection that has not been created with the distributeShardsLike option itself can be used as well in a flexibly sharded database.

Running Queries

For this arangosh example, first insert a few documents into a collection, then create a query and explain it to inspect the execution plan.

arangosh@oneShardDB> for (let i = 0; i < 10000; i++) { db.example.insert({ "value" : i }); }

arangosh@oneShardDB> q = "FOR doc IN @@collection FILTER doc.value % 2 == 0 SORT doc.value ASC LIMIT 10 RETURN doc";

arangosh@oneShardDB> db._explain(q, { "@collection" : "example" })

Query String (88 chars, cacheable: true):
 FOR doc IN @@collection FILTER doc.value % 2 == 0 SORT doc.value ASC LIMIT 10 RETURN doc

Execution plan:
 Id   NodeType                  Site   Est.   Comment
  1   SingletonNode             DBS       1   * ROOT
  2   EnumerateCollectionNode   DBS   10000     - FOR doc IN example   /* full collection scan, 1 shard(s) */   FILTER ((doc.`value` % 2) == 0)   /* early pruning */
  5   CalculationNode           DBS   10000       - LET #3 = doc.`value`   /* attribute expression */   /* collections used: doc : example */
  6   SortNode                  DBS   10000       - SORT #3 ASC   /* sorting strategy: constrained heap */
  7   LimitNode                 DBS      10       - LIMIT 0, 10
  9   RemoteNode                COOR     10       - REMOTE
 10   GatherNode                COOR     10       - GATHER
  8   ReturnNode                COOR     10       - RETURN doc

Indexes used:
 none

Optimization rules applied:
 Id   RuleName
  1   move-calculations-up
  2   move-filters-up
  3   move-calculations-up-2
  4   move-filters-up-2
  5   cluster-one-shard
  6   sort-limit
  7   move-filters-into-enumerate

As it can be seen in the explain output, almost the complete query is executed on the DB-Server (DBS for nodes 1-7) and only 10 documents are transferred to the Coordinator. In case you do the same with a collection that consists of several shards, you get a different result:

arangosh> db._createDatabase("shardedDB")

arangosh> db._useDatabase("shardedDB")

arangosh@shardedDB> db._properties()
{
  "id" : "6010017",
  "name" : "shardedDB",
  "isSystem" : false,
  "sharding" : "flexible",
  "replicationFactor" : 1,
  "writeConcern" : 1,
  "path" : ""
}

arangosh@shardedDB> db._create("example", { numberOfShards : 5})

arangosh@shardedDB> for (let i = 0; i < 10000; i++) { db.example.insert({ "value" : i }); }

arangosh@shardedDB> db._explain(q, { "@collection" : "example" })

Query String (88 chars, cacheable: true):
 FOR doc IN @@collection FILTER doc.value % 2 == 0 SORT doc.value ASC LIMIT 10 RETURN doc

Execution plan:
 Id   NodeType                  Site   Est.   Comment
  1   SingletonNode             DBS       1   * ROOT
  2   EnumerateCollectionNode   DBS   10000     - FOR doc IN example   /* full collection scan, 5 shard(s) */   FILTER ((doc.`value` % 2) == 0)   /* early pruning */
  5   CalculationNode           DBS   10000       - LET #3 = doc.`value`   /* attribute expression */   /* collections used: doc : example */
  6   SortNode                  DBS   10000       - SORT #3 ASC   /* sorting strategy: constrained heap */
 11   RemoteNode                COOR  10000       - REMOTE
 12   GatherNode                COOR  10000       - GATHER #3 ASC  /* parallel, sort mode: heap */
  7   LimitNode                 COOR     10       - LIMIT 0, 10
  8   ReturnNode                COOR     10       - RETURN doc

Indexes used:
 none

Optimization rules applied:
 Id   RuleName
  1   move-calculations-up
  2   move-filters-up
  3   move-calculations-up-2
  4   move-filters-up-2
  5   scatter-in-cluster
  6   distribute-filtercalc-to-cluster
  7   distribute-sort-to-cluster
  8   remove-unnecessary-remote-scatter
  9   sort-limit
 10   move-filters-into-enumerate
 11   parallelize-gather

Without the OneShard feature all documents potentially have to be sent to the Coordinator for further processing. With this simple query this is actually not true, because some other optimizations are performed that reduce the number of documents. But still, a considerable amount of documents has to be transferred from DB-Server to Coordinator only to apply a LIMIT of 10 documents there. The estimate for the RemoteNode is 10,000 in this example, whereas it is 10 in the OneShard case.

ACID Transactions on Leader Shards

ArangoDB’s transactional guarantees are tunable. For transactions to be ACID on the leader shards in a cluster, a few things need to be considered:

• The AQL query or Stream Transaction must be eligible for the OneShard optimization, so that it is executed on a single DB-Server node.
• To ensure durability, enable waitForSync on query level to wait until data modifications have been written to disk.
• The collection option writeConcern: 2 makes sure that a transaction is only successful if at least one follower shard is in sync with the leader shard, for a total of two shard replicas.
• The RocksDB engine supports intermediate commits for larger document operations, potentially breaking the atomicity of transactions. To prevent this for individual queries you can increase intermediateCommitSize (default 512 MB) and intermediateCommitCount accordingly as query option.

Limitations

The OneShard optimization is used automatically for all eligible AQL queries and Stream Transactions.

For AQL queries, any of the following factors currently makes a query unsuitable for the OneShard optimization:

• The query accesses collections with more than a single shard, different leader DB-Servers, or different distributeShardsLike prototype collections
• The query writes into a SatelliteCollection
• The query accesses an edge collection of a SmartGraph
• Usage of AQL functions that can only execute on Coordinators. These functions are:
  • APPLY
  • CALL
  • COLLECTION_COUNT
  • COLLECTIONS
  • CURRENT_DATABASE
  • CURRENT_USER
  • FULLTEXT
  • NEAR
  • SCHEMA_GET
  • SCHEMA_VALIDATE
  • V8
  • VERSION
  • WITHIN
  • WITHIN_RECTANGLE
  • User-defined AQL functions (UDFs)