Features and Improvements in ArangoDB 3.7

The following list shows in detail which features have been added or improved in ArangoDB 3.7. ArangoDB 3.7 also contains several bug fixes that are not listed here.


ArangoSearch was extended to support the LIKE() function and LIKE operator in AQL. This allows to check whether the given search pattern is contained in specified attribute using wildcard matching (_ for any single character and % for any sequence of characters including none):

FOR doc IN viewName
  SEARCH ANALYZER(LIKE(doc.text, "foo%b_r"), "text_en")
  // or
  SEARCH ANALYZER(doc.text LIKE "foo%b_r", "text_en")
  // will match "foobar", "fooANYTHINGbor" etc.
  RETURN doc.text

See ArangoSearch functions

Covering Indexes

It is possible to directly store the values of document attributes in View indexes now via a new View property storedValues (not to be confused with the existing storeValues).

View indexes may fully cover SEARCH queries for improved performance. While late document materialization reduces the amount of fetched documents, this new optimization can avoid to access the storage engine entirely.

  "links": {
    "articles": {
      "fields": {
        "categories": {}
  "primarySort": [
    { "field": "publishedAt", "direction": "desc" }
  "storedValues": [
    { "fields": [ "title", "categories" ] }

In above View definition, the document attribute categories is indexed for searching, publishedAt is used as primary sort order and title as well as categories are stored in the View using the new storedValues property.

FOR doc IN articlesView
  SEARCH doc.categories == "recipes"
  SORT doc.publishedAt DESC
    title: doc.title,
    date: doc.publishedAt,
    tags: doc.categories

The query searches for articles which contain a certain tag in the categories array and returns title, date and tags. All three values are stored in the View (publishedAt via primarySort and the two other via storedValues), thus no documents need to be fetched from the storage engine to answer the query. This is shown in the execution plan as a comment to the EnumerateViewNode: /* view query without materialization */

Execution plan:
 Id   NodeType            Est.   Comment
  1   SingletonNode          1   * ROOT
  2   EnumerateViewNode      1     - FOR doc IN articlesView SEARCH (doc.`categories` == "recipes") SORT doc.`publishedAt` DESC LET #1 = doc.`publishedAt` LET #7 = doc.`categories` LET #5 = doc.`title`   /* view query without materialization */
  5   CalculationNode        1       - LET #3 = { "title" : #5, "date" : #1, "tags" : #7 }   /* simple expression */
  6   ReturnNode             1       - RETURN #3

Indexes used:

Optimization rules applied:
 Id   RuleName
  1   move-calculations-up
  2   move-calculations-up-2
  3   handle-arangosearch-views

See ArangoSearch Views.

Stemming support for more languages

The Snowball library was updated to the latest version 2, adding stemming support for the following languages:

  • Arabic (ar)
  • Basque (eu)
  • Catalan (ca)
  • Danish (da)
  • Greek (el)
  • Hindi (hi)
  • Hungarian (hu)
  • Indonesian (id)
  • Irish (ga)
  • Lithuanian (lt)
  • Nepali (ne)
  • Romanian (ro)
  • Serbian (sr)
  • Tamil (ta)
  • Turkish (tr)

Create a custom Analyzer and set the locale accordingly in the properties, e.g. "el.utf-8" for Greek. arangosh example:

var analyzers = require("@arangodb/analyzers");

analyzers.save("text_el", "text", {
  locale: "el.utf-8",
  stemming: true,
  case: "lower",
  accent: false,
  stopwords: []
}, ["frequency", "norm", "position"]);

db._query(`RETURN TOKENS("αυτοκινητουσ πρωταγωνιστούσαν", "text_el")`)
// [ [ "αυτοκινητ", "πρωταγωνιστ" ] ]

Also see Analyzers: Supported Languages

Condition Optimization Option

The SEARCH operation in AQL accepts a new option conditionOptimization to give users control over the search criteria optimization:

FOR doc IN myView
  SEARCH doc.val > 10 AND doc.val > 5 /* more conditions */
  OPTIONS { conditionOptimization: "none" }
  RETURN doc

By default, all conditions get converted into disjunctive normal form (DNF). Numerous optimizations can be applied, like removing redundant or overlapping conditions (such as doc.val > 10 which is included by doc.val > 5). However, converting to DNF and optimizing the conditions can take quite some time even for a low number of nested conditions which produce dozens of conjunctions / disjunctions. It can be faster to just search the index without optimizations.

See SEARCH operation.

Primary Sort Compression Option

There is a new option primarySortCompression which can be set on View creation to enable or disable the compression of the primary sort data:

  "primarySort": [
    { "field": "date", "direction": "desc" },
    { "field": "title", "direction": "asc" }
  "primarySortCompression": "none",

It defaults to LZ4 compression ("lz4"), which was already used in ArangoDB v3.5 and v3.6. Set it to "none" on View creation to trade space for speed.

See ArangoSearch Views.


When doing joins involving graph traversals, shortest path or k-shortest paths computation in an ArangoDB cluster, data has to be exchanged between different servers. In particular graph traversals are usually executed on a Coordinator, because they need global information. This results in a lot of network traffic and potentially slow query execution.

SatelliteGraphs are the natural extension of the concept of SatelliteCollections to graphs. All of the usual benefits and caveats apply. SatelliteGraphs are synchronously replicated to all DB-Servers that are part of a cluster, which enables DB-Servers to execute graph traversals locally. This includes (k-)shortest path(s) computation and possibly joins with traversals and greatly improves performance for such queries.

SatelliteGraphs are only available in the Enterprise Edition.

Disjoint SmartGraphs

SmartGraphs have been extended with a new option isDisjoint. A Disjoint SmartGraph prohibits edges connecting different SmartGraph components. If your graph doesn’t need edges between vertices with different SmartGraph attribute values, then you should enable this option. This topology restriction allows the query optimizer to improve traversal execution times, because in many cases the execution can be pushed down to a single DB-Server.

Disjoint SmartGraphs are only available in the Enterprise Edition.


Subquery optimizations

The execution process of AQL has been refactored internally. This especially pays off in subqueries. It will allow for more optimizations and better batching of requests.

The first stage of this refactoring has been part of 3.6 already where some subqueries have gained a significant performance boost. 3.7 takes the next step in this direction. AQL can now combine skipping and producing of outputs in a single call, so all queries with a LIMIT offset or the fullCount option enabled will benefit from this change straight away. This also holds true for subqueries, hence the existing AQL optimizer rule splice-subqueries is now able to optimize all subqueries and is enabled by default.

The query planner can now also reuse internal registers that were allocated for storing temporary results inside subqueries, but not outside of subqueries.

Count optimizations

Subqueries can now use an optimized code path for counting documents if they are supposed to only return the number of matching documents. The optimization will be triggered for read-only subqueries that use a full collection scan or an index scan, without any additional filtering on document attributes (early pruning or document post-filtering) and without using LIMIT.

The optimization will help in the following situation (in case subCollection is an edge collection):

FOR doc IN collection
  LET count = COUNT(
    FOR sub IN subCollection
      FILTER sub._from == doc._id
      RETURN sub

The restrictions are that the subquery result must only be used with the COUNT/LENGTH AQL function and not for anything else. The subquery itself must be read-only (no data-modification subquery), not use nested FOR loops, no LIMIT clause and no FILTER condition or calculation that requires accessing document data. Accessing index data is supported for filtering (as in the above example that would use the edge index), but not for further calculations.

In case a subquery does not match these criteria, it will not use the optimized code path for counting, but will execute normally.

If the optimization is triggered, it will show up in the query execution plan under the rule name optimize-count, and the subquery’s FOR loop will be marked with a with count optimization tag.

Traversal optimizations

Graph traversal performance is improved via some internal code refactoring:

  • Traversal cursors are reused instead of recreated from scratch, if possible. This can save lots of calls to the memory management subsystem.
  • Unnecessary checks have been removed from the cursors, by ensuring some invariants beforehand.
  • Each vertex lookup needs to perform slightly less work.

The traversal speedups observed by these changes alone were around 8 to 10% for single-server traversals and traversals in OneShard setups. Cluster traversals will also benefit from these changes, but to a lesser extent. This is because the network roundtrips have a higher share of the total query execution times there.

Traversal performance can be further improved by not fetching the visited vertices from the storage engine in case the traversal query does not refer to them. For example, in the query:

FOR v, e, p IN 1..3 OUTBOUND 'collection/startVertex' edges

…the vertex variable (v) is never accessed, making it unnecessary to fetch the vertices from storage. If this optimization is applied, the traversal node will be marked with /* vertex optimized away */ in the query’s execution plan output. Unused edge and path variables (e and p) were already optimized away in previous versions by the optimize-traversals optimizer rule.

Traversal collection restrictions

AQL traversals now accept the options vertexCollections and edgeCollections to restrict the traversal to certain vertex or edge collections.

The use case for vertexCollections is to not follow any edges that will point to other than the specified vertex collections, e.g.

FOR v, e, p IN 1..3 OUTBOUND 'products/123' components
  OPTIONS { vertexCollections: [ "bolts", "screws" ] }

The traversal’s start vertex is always considered valid, even if it not stored in any of the collections listed in the vertexCollections option.

The use case for edgeCollections is to not take into consideration any edges from edge collections other than the specified ones, e.g.

FOR v, e, p IN 1..3 OUTBOUND 'products/123' GRAPH 'components'
  OPTIONS { edgeCollections: [ "productsToBolts", "productsToScrews" ] }

This is mostly useful in the context of named graphs, when the named graph contains many edge collections. Not restricting the edge collections for the traversal will make the traversal search for edges in all edge collections of the graph, which can be expensive. In case it is known that only certain edges from the named graph are needed, the edgeCollections option can be a handy performance optimization. It can replace less efficient post-filtering:

FOR v, e, p IN 1..3 OUTBOUND 'products/123' GRAPH 'components'
                       OR IS_SAME_COLLECTION("productsToScrews", CURRENT)] ALL == true

Also see AQL Traversal Options

Traversal parallelization (Enterprise Edition)

Nested traversals that run on a single server or a cluster DB-Server can now be executed in parallel.

Traversals have a new option parallelism which can be used to specify the level of parallelism:

FOR doc IN outerCollection
  FOR v, e, p IN 1..3 OUTBOUND doc._id GRAPH 'components'
  OPTIONS { parallelism: 4 }

Traversal parallelism is opt-in. If not specified, the parallelism value implicitly defaults to 1, which means no parallelism will be used. The maximum value for parallelism is capped to the number of available cores on the target machine.

Due to the required synchronization for splitting up traversal inputs and merging results, using traversal parallelization may incur some overhead. So it is not a silver bullet for all use cases. However, parallelizing a traversal is normally useful when there are many inputs (start vertices) that the nested traversal can work on concurrently. This is often the case when a nested traversal is fed with several tens of thousands of start vertices, which can then be distributed randomly to worker threads for parallel execution.

Right now, traversal parallelization is limited to traversals in single server deployments and to cluster traversals that are running in a OneShard setup. Cluster traversals that run on a coordinator node and SmartGraph traversals are currently not parallelized.

See Graph traversal options

AQL functions added

The following AQL functions have been added in ArangoDB 3.7:

Syntax enhancements

AQL now supports trailing commas in array and object definitions.

This is especially convenient for editing multi-line array/object definitions, since there doesn’t need to be a distinction between the last element and all others just for the comma. That means definitions such as the following are now supported:

  3, // trailing comma
  "a": 1,
  "b": 2,
  "c": 3, // trailing comma

Previous versions of ArangoDB did not support trailing commas in AQL queries and threw query parse errors when they were used.

AQL datetime parsing

The performance of parsing ISO 8601 date/time string values in AQL has improved significantly thanks to a specialized parser, replacing a regular expression.

Ternary operator

Improved the lazy evaluation capabilities of the ternary operator. If the second operand is left out, the expression of the condition is only evaluated once now, instead of once more for the true branch.

Other AQL improvements

“remove-unnecessary-calculations” optimizer rule

The AQL query optimizer now tries to not move potentially expensive AQL function calls into loops in the remove-unnecessary-calculations rule.

For example, in the query:

LET x = NOOPT(1..100)
LET values = SORTED(x)
FOR j IN 1..100 
  FILTER j IN values

… there is only one use of the values variable. So the optimizer can remove that variable and replace the filter condition with FILTER j IN SORTED(x). However, that would move the potentially expensive function call into the inner loop, which could be a pessimization.

The optimizer will not move the calculation of values into the loop anymore when it merges calculations in the remove-unnecessary-calculations optimizer rule.

“move-calculations-down” optimizer rule

The existing AQL optimizer rule move-calculations-down is now able to also move unrelated subqueries beyond SORT and LIMIT instructions, which can help avoid the execution of subqueries for which the results are later discarded.

For example, in the query:

FOR doc IN collection1
  LET sub1 = FIRST(FOR sub IN collection2 FILTER sub.ref == doc._key RETURN sub)
  LET sub2 = FIRST(FOR sub IN collection3 FILTER sub.ref == doc._key RETURN sub)

  SORT sub1
  LIMIT 10
  RETURN { doc, sub1, sub2 }

… the execution of the sub2 subquery can be delayed to after the SORT and LIMIT. The query optimizer will automatically transform this query into the following:

FOR doc IN collection1
  LET sub1 = FIRST(FOR sub IN collection2 FILTER sub.ref == doc._key RETURN sub)
  SORT sub1
  LIMIT 10

  LET sub2 = FIRST(FOR sub IN collection3 FILTER sub.ref == doc._key RETURN sub)
  RETURN { doc, sub1, sub2 }


Incremental Plan Updates

In ArangoDB clusters, the Agency is the single source of truth for data definition (databases, collections, shards, indexes, views), the cluster configuration and the current cluster setup (e.g. shard distribution, shard leadership).

Coordinators and DB-Servers in the cluster maintain a local cache of the Agency’s information, in order to access it in a performant way whenever they need any information about the setup. However, any change that was applied to the Plan and Current sections in the Agency led to the server-local caches being invalidated, which triggered a full reload of either Plan or Current by all Coordinators and DB-Servers. The size of Plan and Current is proportional to the number of database objects, so fully reloading the data from the Agency is an expensive operation for deployments which have a high number of databases, collections, or shards.

In ArangoDB 3.7 the mechanism for filling the local caches on Coordinators and DB-Servers with Agency data has changed fundamentally. Instead of invalidating the entire cache and reloading the full Plan or Current section on every change, each server is now using a permanent connection to the Agency and uses it to poll for changes. Changes to the Agency data are sent over these connections as soon as they are applied in the Agency, meaning that Coordinators and DB-Servers can apply them immediately and incrementally. This removes the need for full reloads. As a consequence, a significant reduction of overall network traffic between Agents and other cluster nodes is expected, plus a significant reduction in CPU usage on Agents for assembling and sending the Plan or Current parts. Another positive side effect of this modification is that changes made to Agency data should propagate faster in the cluster.

Parallel Move Shard

Shards can now move in parallel. The old locking mechanism was replaced by a read-write lock and thus allows multiple jobs for the same destination server. The actual transfer rates are still limited on DB-Server side but there is a huge overall speedup. This also affects CleanOutServer and ResignLeadership jobs.


Schema Validation for Documents

ArangoDB now supports validating documents on collection level using JSON Schema (draft-4).

In order to enforce a certain document structure in a collection we have introduced the schema collection property. It expects an object comprised of a rule (JSON Schema object), a level and a message that will be used when validation fails. When documents are validated is controlled by the validation level, which can be none (off), new (insert only), moderate (on insert and modification, but existing documents can remain invalid) or strict (always).

See: Schema Validation

HTTP/2 support

The server now supports upgrading connections from HTTP 1.1 to HTTP 2. This should improve ArangoDBs compatibility with various L7 load balancers and modern cloud platforms like Kubernetes.

We also expect improved request throughput in cases where there are many concurrent requests.

See: HTTP Switching Protocols

Server Name Indication (Enterprise Edition)

Sometimes it is desirable to have the same server use different server keys and certificates when it is contacted under different names. This is possible with the Server Name Indication (SNI) TLS extension. It is now supported by ArangoDB using a new startup option --ssl.server-name-indication.

JWT secret rotation (Enterprise Edition)

There are now new APIs and startup options for JWT secrets. The new option --server.jwt-secret-folder can be used to specify a path for more than one JWT secret file.

Additionally the /_admin/server/jwt API can be used to reload the JWT secrets of a local arangod process without having to restart it (hot-reload). This may be used to roll out new JWT secrets throughout an ArangoDB cluster.

TLS key and certificate rotation

It is now possible to change the TLS keyfile (secret key as well as public certificates) at run time. The API POST /_admin/server/tls basically makes the arangod server reload the keyfile from disk.

Furthermore, one can query the current TLS setup at runtime with the GET /_admin/server/tls API. The public certificates as well as a SHA-256 hash of the private key is returned.

This allows rotation of TLS keys and certificates without a server restart.

Encryption at rest key rotation (Enterprise Edition)

It is possible to change the user supplied encryption key via the HTTP API by sending a POST request without payload to the new endpoint /_admin/server/encryption. The file supplied via --rocksdb.encryption-keyfile will be reloaded and the internal encryption key will be re-encrypted with the new user key. Note that this API is turned off by default. It can be enabled via the --rocksdb.encryption-key-rotation startup option.

Similarly the new option --rocksdb.encryption-keyfolder can be used to supply multiple user keys. By default, the first available user-supplied key will be used as the internal encryption key. Alternatively, if the option --rocksdb.encryption-gen-internal-key is set to true, a random internal key will be generated and encrypted with each of the provided user keys.

Please be aware that the encryption at rest key rotation is an experimental feature, and its APIs and behavior are still subject to change.

Insert-Update and Insert-Ignore

ArangoDB 3.7 adds an insert-update operation that is similar to the already existing insert-replace functionality. A new overwriteMode flag has been introduced to control the type of the overwrite operation in case of colliding primary keys during the insert.

In the case of overwriteMode: "update", the parameters keepNull and mergeObjects can be provided to control the update operation.

There is now also an insert-ignore operation that allows insert operations to do nothing in case of a primary key conflict. This operation is an efficient way of making sure a document with a specific primary key exists. If it does not exist already, it will be created as specified. Should the document exist already, nothing will happen and the insert will return without an error. No write operations happens in this case, and only a single primary key lookup needs to be performed in the storage engine. This makes the insert-ignore operation the most efficient way existing insert-replace functionality. A new overwriteMode flag has been introduced to control the type of the overwrite operation in case of colliding primary keys during the insert.

The query options are available in AQL, the JS API and HTTP API.

Override detected total memory and CPU cores

arangod detects the total amount of RAM present on the system and calculates various default sizes based on this value. If you run it alongside other services or in a container with a RAM limitation for its cgroup, then you probably don’t want the server to detect and use all available memory.

An environment variable ARANGODB_OVERRIDE_DETECTED_TOTAL_MEMORY can now be set to restrict the amount of memory it will detect (also available in v3.6.3).

An environment variable ARANGODB_OVERRIDE_DETECTED_NUMBER_OF_CORES can be set to restrict the number of CPU cores that are visible to arangod.

See ArangoDB Server Environment Variables

RocksDB storage engine options exposed

Multiple additional RocksDB configuration options are now exposed to be configurable in arangod:

  • --rocksdb.cache-index-and-filter-blocks to make the RocksDB block cache quota also include RocksDB memtable sizes
  • --rocksdb.cache-index-and-filter-blocks-with-high-priority to use cache index and filter blocks with high priority making index and filter blocks be less likely to be evicted than data blocks
  • --rocksdb.pin-l0-filter-and-index-blocks-in-cache to make filter and index blocks be pinned and only evicted from cache when the table reader is freed
  • --rocksdb.pin-top-level-index-and-filter to make the top-level index of partitioned filter and index blocks pinned and only be evicted from cache when the table reader is freed
  • --rocksdb.target-file-size-base: Per-file target file size for compaction (in bytes). the actual target file size for each level is --rocksdb.target-file-size-base multiplied by --rocksdb.target-file-size-multiplier ^ (level - 1)
  • --rocksdb.target-file-size-multiplier: Multiplier for --rocksdb.target-file-size, a value of 1 means that files in different levels will have the same size (default)


A new algorithm "wcc" has been added to Pregel to find connected components.

There are now three algorithms to find connected components in a graph:

  1. If your graph is effectively undirected (you have edges in both directions between vertices) then the simple connected components algorithm named "connectedcomponents" is suitable.

    It is a very simple and fast algorithm, but will only work correctly on undirected graphs. Your results on directed graphs may vary, depending on how connected your components are.

  2. To find weakly connected components (WCC) you can now use the new algorithm named "wcc". Weakly connected means that there exists a path from every vertex pair in that component.

    This algorithm will work on directed graphs but requires a greater amount of traffic between your DB-Servers.

  3. to find strongly connected components (SCC) you can use the algorithm named "scc". Strongly connected means every vertex is reachable from any other vertex in the same component.

    The algorithm is more complex than the WCC algorithm and requires more memory, because each vertex needs to store much more state.

Also see Pregel


Foxx endpoints now provide the methods securityScheme, securityScope and security to allow defining Swagger security schemes.

Foxx routes now always have a Swagger operationId. If the route is unnamed, a distinct operationId will be generated based on the HTTP method and URL.


V8 and ICU library upgrades

The bundled V8 JavaScript engine was upgraded to version 7.9.317. The bundled Unicode character handling library ICU was upgraded to version 64.2.

The resource usage of V8 has improved a lot. Memory usage is down by 15%, spawning a new Isolate has become almost 10 times faster.

Here is the list of improvements that may matter to you as an ArangoDB user:

  • JSON.parse improvements: JSON parsing is roughly 60% faster compared to ArangoDB v3.6. Parsing JSON is generally faster than parsing JavaScript because of the lower syntactic complexity, but with the additional speedup of the JSON parser you should consider to use JSON.parse(string) over JavaScript variable declarations for complex data:
    // Parsing a JSON string
    let structuredVar = JSON.parse('{"foo": "bar", …}');
    // instead of using an object literal
    let structuredVar = {foo: "bar", };

    Also see Embedding JSON into JavaScript programs with JSON.parse.

  • BigInt support in formatter: Large integer numbers are now supported in number formatters:
    const formatter = new Intl.NumberFormat('fr');

    This no longer throws an Cannot convert a BigInt value to a number error. Note that ArangoDB does not support BigInt in general but only in JavaScript contexts. AQL, JSON etc. do not support BigInt.

  • Object.fromEntries support: Performs the inverse operation of Object.entries():
    const object = { x: 42, y: 50 };
    const entries = Object.entries(object);
    // → [['x', 42], ['y', 50]]
    const result = Object.fromEntries(entries);
    // → { x: 42, y: 50 }
  • Underscores for better readability of large numbers:
    1_000_000_000_000 // → equals 1000000000000
  • matchAll support for strings: A convenient generator for a match object for each match:
    const string = 'Favorite GitHub repos: tc39/ecma262 v8/v8.dev';
    const regex = /\b(?<owner>[a-z0-9]+)\/(?<repo>[a-z0-9\.]+)\b/g;
    for (const match of string.matchAll(regex)) {
      console.log(`${match[0]} at ${match.index} with '${match.input}'`);
      console.log(`→ owner: ${match.groups.owner}`);
      console.log(`→ repo: ${match.groups.repo}`);
  • ICU supports more languages and characters (Unicode 12.1), emoji handling was improved

Also see:

JavaScript APIs

The query helper was extended to support passing query options:

require("@arangodb").query( { maxRuntime: 1 } )`RETURN SLEEP(2)`

Web UI

The interactive description of ArangoDB’s HTTP API (Swagger UI) shows the endpoint and model entries collapsed by default now for a better overview.

The bundled version of Swagger has been upgraded to 3.25.1. This change has also been backported to ArangoDB v3.6.4.


The amount of exported metrics for monitoring has been extended and is now available in a format compatible with Prometheus. You can now easily scrape on /_admin/metrics. See Metrics HTTP API.

The following metrics have been added in ArangoDB 3.7:

Label Description
arangodb_agency_append_hist Agency RAFT follower append histogram
arangodb_agency_commit_hist Agency RAFT commit histogram
arangodb_agency_compaction_hist Agency compaction histogram
arangodb_agency_local_commit_index This agent’s commit index
arangodb_agency_log_size_bytes Agency replicated log size (bytes)
arangodb_agency_read_no_leader Agency read no leader
arangodb_agency_read_ok Agency read ok
arangodb_agency_supervision_accum_runtime_msec Accumulated Supervision Runtime
arangodb_agency_supervision_accum_runtime_wait_for_replication_msec Accumulated Supervision wait for replication time
arangodb_agency_supervision_failed_server_count Counter for FailedServer jobs
arangodb_agency_supervision_runtime_msec Agency Supervision runtime histogram (ms)
arangodb_agency_supervision_runtime_wait_for_replication_msec Agency Supervision wait for replication time (ms)
arangodb_agency_term Agency’s term
arangodb_agency_write_hist Agency write histogram (ms)
arangodb_agency_write_no_leader Agency write no leader
arangodb_agency_write_ok Agency write ok
arangodb_agencycomm_request_time_msec Request time for Agency requests
arangodb_aql_slow_query Number of AQL slow queries
arangodb_aql_total_query_time_msec Total execution time of all queries
arangodb_client_connection_statistics_io_time_bucket Request time needed to answer a request (ms)
arangodb_client_connection_statistics_io_time_count Request time needed to answer a request (ms)
arangodb_client_connection_statistics_io_time_sum Request time needed to answer a request (ms)
arangodb_client_connection_statistics_queue_time_bucket Request time needed to answer a request (ms)
arangodb_client_connection_statistics_queue_time_count Request time needed to answer a request (ms)
arangodb_client_connection_statistics_queue_time_sum Request time needed to answer a request (ms)
arangodb_client_connection_statistics_request_time_bucket Request time needed to answer a request (ms)
arangodb_client_connection_statistics_request_time_count Request time needed to answer a request (ms)
arangodb_client_connection_statistics_request_time_sum Request time needed to answer a request (ms)
arangodb_client_connection_statistics_total_time_bucket Total time needed to answer a request (ms)
arangodb_client_connection_statistics_total_time_count Total time needed to answer a request (ms)
arangodb_client_connection_statistics_total_time_sum Total time needed to answer a request (ms)
arangodb_dropped_followers_count Number of drop-follower events
arangodb_heartbeat_failures Counting failed heartbeat transmissions
arangodb_heartbeat_send_time_msec Time required to send heartbeat (ms)
arangodb_http_request_statistics_async_requests Number of asynchronously executed HTTP requests
arangodb_http_request_statistics_http_delete_requests Number of HTTP DELETE requests
arangodb_http_request_statistics_http_get_requests Number of HTTP GET requests
arangodb_http_request_statistics_http_head_requests Number of HTTP HEAD requests
arangodb_http_request_statistics_http_options_requests Number of HTTP OPTIONS requests
arangodb_http_request_statistics_http_patch_requests Number of HTTP PATCH requests
arangodb_http_request_statistics_http_post_requests Number of HTTP POST requests
arangodb_http_request_statistics_http_put_requests Number of HTTP PUT requests
arangodb_http_request_statistics_other_http_requests Number of other HTTP requests
arangodb_http_request_statistics_total_requests Total number of HTTP requests
arangodb_load_current_accum_runtime_msec Accumulated Current loading time (ms)
arangodb_load_current_runtime Current loading runtimes
arangodb_load_plan_accum_runtime_msec Accumulated runtime of Plan loading (ms)
arangodb_load_plan_runtime Plan loading runtimes
arangodb_maintenance_action_accum_queue_time_msec Accumulated action queue time
arangodb_maintenance_action_accum_runtime_msec Accumulated action runtime
arangodb_maintenance_action_done_counter Counter of action that are done and have been removed from the registry
arangodb_maintenance_action_duplicate_counter Counter of action that have been discarded because of a duplicate
arangodb_maintenance_action_failure_counter Failure counter for the action
arangodb_maintenance_action_queue_time_msec Time spend in the queue before execution
arangodb_maintenance_action_registered_counter Counter of action that have been registered in the action registry
arangodb_maintenance_action_runtime_msec Time spend execution the action
arangodb_maintenance_agency_sync_accum_runtime_msec Accumulated runtime of agency sync phase
arangodb_maintenance_agency_sync_runtime_msec Total time spend on agency sync
arangodb_maintenance_phase1_accum_runtime_msec Accumulated runtime of phase one
arangodb_maintenance_phase1_runtime_msec Maintenance Phase 1 runtime histogram
arangodb_maintenance_phase2_accum_runtime_msec Accumulated runtime of phase two
arangodb_maintenance_phase2_runtime_msec Maintenance Phase 2 runtime histogram
arangodb_scheduler_awake_threads Number of awake worker threads
arangodb_scheduler_num_worker_threads Number of worker threads
arangodb_scheduler_queue_full_failures Number of times the scheduler queue was full and a task/request was rejected
arangodb_scheduler_queue_length Server’s internal queue length
arangodb_server_statistics_physical_memory Physical memory in bytes
arangodb_server_statistics_server_uptime Number of seconds elapsed since server start
arangodb_shards_leader_count Number of leader shards on this machine
arangodb_shards_not_replicated Number of shards not replicated at all
arangodb_shards_out_of_sync Number of leader shards not fully replicated
arangodb_shards_total_count Number of shards on this machine
arangodb_v8_context_alive Number of V8 contexts currently alive
arangodb_v8_context_busy Number of V8 contexts currently busy
arangodb_v8_context_created Number of V8 contexts created
arangodb_v8_context_destroyed Number of V8 contexts destroyed
arangodb_v8_context_dirty Number of V8 contexts currently dirty (waiting for garbage collection)
arangodb_v8_context_enter_failures Number of times a V8 context could not be entered/acquired
arangodb_v8_context_entered Number of times a V8 context was successfully entered
arangodb_v8_context_exited Number of times a V8 context was successfully exited
arangodb_v8_context_free Number of V8 contexts currently free
arangodb_v8_context_max Maximum number of concurrent V8 contexts allowed
arangodb_v8_context_min Minimum number of concurrent V8 contexts allowed

Client tools

arangodump and arangorestore will now fail when using the --collection option and none of the specified collections actually exist in the database (on dump) or in the dump to restore (on restore). In case only some of the specified collections exist, arangodump / arangorestore will issue warnings about the invalid collections, but will continue to work for the valid collections.

These change were made to make end users more aware of that the executed commands for dumping or restoring data refer to non-existing collections and that backup or restore operations are potentially incomplete.

MMFiles storage engine

ArangoDB 3.7 does not contain the MMFiles storage engine anymore. In ArangoDB 3.7, the only available storage engine is the RocksDB storage engine, which is the default storage engine in ArangoDB since version 3.4. The MMFiles storage engine had been deprecated since the release of ArangoDB 3.6.

Any deployments that use the MMFiles storage engine will need to be migrated to the RocksDB storage engine using ArangoDB 3.6 (or earlier versions) in order to upgrade to ArangoDB 3.7.

All storage engine selection functionality has also been removed from the ArangoDB package installers. The RocksDB storage engine will be selected automatically for any new deployments created with ArangoDB 3.7.

This change simplifies the installation procedures and internal code paths.

Internal changes

Upgraded bundled RocksDB library version

The bundled version of the RocksDB library has been upgraded from 6.2 to 6.8.

Upgraded bundled OpenLDAP library version

The OpenLDAP version used for the LDAP integration in the ArangoDB Enterprise Edition has been upgraded to 2.4.50. This change has been backported to ArangoDB v3.6.5 as well.

Added libunwind library dependency

The Linux builds of ArangoDB now use the third-party library libunwind to get backtraces and to symbolize stack frames.

Building with libunwind can be turned off at compile time using the -DUSE_LIBUNWIND CMake variable.

Removed libcurl library dependency

The compile-time dependency on libcurl was removed. Cluster-internal communication is now performed using fuerte instead of libcurl.

Crash handler

The Linux builds of the arangod executable contain a built-in crash handler The crash handler is supposed to log basic crash information to the ArangoDB logfile in case the arangod process receives one of the signals SIGSEGV, SIGBUS, SIGILL, SIGFPE or SIGABRT. SIGKILL signals, which the operating system can send to a process in case of OOM (out of memory), are not interceptable and thus cannot be intercepted by the crash handler.

In case the crash handler receives one of the mentioned interceptable signals, it will write basic crash information to the logfile and a backtrace of the call site. The backtrace can be provided to the ArangoDB support for further inspection. Note that backtaces are only usable if debug symbols for ArangoDB have been installed as well.

After logging the crash information, the crash handler will execute the default action for the signal it has caught. If core dumps are enabled, the default action for these signals is to generate a core file. If core dumps are not enabled, the crash handler will simply terminate the program with a non-zero exit code.

The crash handler can be disabled at server start by setting the environment variable ARANGODB_OVERRIDE_CRASH_HANDLER to an empty string, 0 or off.

Also see:

Supported compilers

Manually compiling ArangoDB from source will require a C++17-ready compiler.

Older versions of g++ that could be used to compile previous versions of ArangoDB, namely g++7, cannot be used anymore for compiling ArangoDB. g++9.2, g++9.3 and g++10 are known to work, and are the preferred compilers to build ArangoDB under Linux.

Under macOS, the official compiler is clang with a minimal target of macOS 10.14 (Mojave).

Under Windows, use the Visual C++ compiler of Visual Studio 2019 v16.5.0 or later. VS 2017 might still work, but is not officially supported any longer.

Documentation generation

The following features have been added for auto-generating documentation:

  • the --dump-options command for arangod and the client tools now also emits an attribute os which indicates on which operating system(s) the respective options are supported.
  • the --dump-options command for arangod now also emits an attribute component which indicates for which node type(s) the respective options are supported (single server, coordinator, dbserver, agent).