Regarding gcc/g++, there seems to be support only for C. From https://gcc.gnu.org/wiki/DavidMalcolm/StaticAnalyzer:

> Initial implementation was added in GCC 10; major rewrite occurred in GCC 11. > Only C is currently supported (I hope to support C++ in GCC 13, but it is out-of-scope for GCC 12)

Looking forward to seeing C++ support added to it!

It worked out of the box, because fbinfer can tap into the compile commands database that can easily be generated by CMake and other build tools.

The tool has nice output about its current progress, but even when running with many threads, its long translation and analysis durations made it somewhat impractical to use on a large project. I understand that later it can run with incremental changes, and can reuse some data from previous runs. In this case it probably may be better suited. But the first-time analysis of a large project is very time-consuming. I am not saying this is unique to fbinfer. Other static analyzers tend to have the same problem. To be fair, I haven't yet inspected the tool's various options, which can potentially speed up the analysis.

The initial report the tool created contained several findings. Mainly "potential" nullptr dereferences and a few "potential" data races. After manual inspection these all turned out to be false positives. However, the tool also found several dead stores, which turned out to be actual dead stores. So it is at least helpful w.r.t.

From my perspective, the tool has potential. Probably some of the false positives can be turned off via configuration, and using it for incremental analysis may also reduce its runtime so that it becomes tolerable for larger projects.

> We're continuing to investigate connection issues for customers, and have upgraded the incident on our side to reflect an outage in service. All hands are on deck on our end to further investigate. We'll be back in a half hour to keep you posted. > Jan 4, 5:20 PM GMT+1

Pretty much every substance will have bad effects on your health when overdosing. But thats not to say that lower doses will produce these negative effects as well.

Sola dosis facit venenum.

AWS DocumentDB seems to be pretty much tied to the MongoDB API right now... So At the moment this will somewhat limit its functionality. However, they will not stand still and probably also extend into the multi-model space at some point. Apart from that, not everyone will be willing to pay for DocumentDB or have their data located in Amazon datacenters.

I could imagine that they didn't build DocumentDB from the ground up.

DocumentDB is probably just a MongoDB compatible API for one of their base services (S3 or DynamoDB).

As far as I know, they build Serverless Aurora on top of S3, with the help of S3 select. So they will probably just create another custom-DB compatible API if they have the impression that this custom DB becomes the next big thing.

It's an implementation of its own, leveraging many the base building blocks and infrastructure Amazon has created.

DocumentDB is currently tied to the MongoDB 3.6 API, that means all the transactional extensions MongoDB has added recently is not present in DocumentDB (yet).

Many search engines access data stored in JSON format. Hence integrating a search engine like ArangoSearch as an additional layer on top of the existing data models is no magic but makes a lot of sense. Allowing to combine models with search is then rather an obvious task for us.

For example, a specialized OLAP database which knows about the schema of the data can employ much more streamlined storage and query operators, so it should have a "natural" performance advantage.

However, a very specialized database may later lock you in to something, and in case you need something different, you will end up with running multiple different special-purpose databases.

Not saying this is necessarily bad (or good), but it is at least one aspect to consider how many different databases to you want to operate & manage in your stack.

AQL is versioned along with the database core, as sometimes features are added to AQL which the database core must also support and vice versa. However, during further development of AQL and the database core, one of the major goals is to keep it always downwards-compatible, meaning that existing AQL queries are expected to work and behave identically in newer versions of the database (but ideally run faster or are better optimized there).

We were absolute idiots, but I still think a data warehouse should be idiot-proof, which is why I like SQL.

I read through the documentation for ArangoDB and I would be concerned about the lack of native strict type definitions and referencing in AQL, as well as the dearth of type availability in ArangoDB in general. Is this a design decision related to not supporting data/database administration, or something to be added later to the roadmap?

It sounds like if you support write-intensive paths through the database, it would be considered an OLTP database for some OLTP workloads; do you publish TPC-C benchmarks anywhere? What about resource utilization?

Is there a particular reason to support JavaScript first? Is it because Swagger has JavaScript-first support, or a different reason?

To keep things simple and manageable, we originally started with AQL just being a language for querying the database. It was extended years ago to support data manipulation operations. I don't exclude the possibility that at some point it will support database administration or DDL commands, however, I am just one of the developers and not the product manager. And you are right about the main use case being OLTP workloads. For OLAP use cases, dedicated analytical databases (with fixed data schemas) are probably superior, because they can run much more specialized and streamlined operations on the data. To my best knowledge we never published any TPC benchmark results somewhere. I think it's possible to implement TPC-C even without SQL, however, implementing the full benchmark is a huge amount of work, so we never did...

Apart from that, ArangoDB comes with a JavaScript-enabled shell (arangosh) that can be used for scripting and automating database operations.

I think there have been various issues with the cluster stability 1.5 years ago, and since then we have put great efforts into making the database much more robust and faster. Many man-years have been dedicated to this since 2017.

1.5 years ago we were shipping release 3.1, which is out of service already. Since then, we have released

* ArangoDB 3.2: this release provided the RocksDB storage engine, which improves parallelism and memory management compared to our traditional mostly-memory storage engine * ArangoDB 3.3: with a new deployment mode (active failover), plus ease-of-use and replication improvements (e.g. cross-datacenter replication) * ArangoDB 3.4: latest release, for which we put great emphasis on performance improvements, namely for the RocksDB storage engine (which now also is the default engine in ArangoDB)

In all of the above releases we also worked on improving AQL query execution plans, in order to make queries perform faster in both single server and cluster deployments. Working on the query optimizer and query execution plan improvements is obviously a never-ending task, and not only did we achieved a lot here since 2017, but we still have a lot of ideas for further improvements in this area. So there are more improvements to be expected for the following releases.

All that said, I think it is clear now that my intention is to show that things should have improved a lot compared to the situation 1.5 y ago, and that we will always be working hard to make ArangoDB a better product.

To be on the safe side, it is a good idea to consult the list of incompatible changes/changed behavior before upgrading: https://github.com/arangodb/arangodb/blob/3.4/Documentation/...

That may seem like a huge list at first, but many items on it should actually be minorities.

More beef to be added to this until the GA release.

The benefits of distributed COLLECT will come into play for queries that can push the aggregate operations onto the shards. Previous versions of ArangoDB shipped all documents from the database servers to the coordinator, so the coordinator would do the central aggregation of the results from all shards to produce the result.

With distributed COLLECT we now create an additional shard-local COLLECT operation that performs part of the aggregation on the shards already. This allows sending just the aggregated per-shard results to the coordinator, so the coordinator can finally perform an aggregation of the per-shard aggregates.

This will be beneficial in many cases when the per-shard aggregated result is much smaller than the non-aggregated per-shard result.

Following is a very simple example. Let's say you have a collection "test" with 5 shards and 500k simple documents that have just one numeric attribute (plus the three system attributes "_key", "_id" and "_rev"):

    db._create("test", { numberOfShards: 5 }); 
    for (i = 0; i < 500000; ++i) {
      db.test.insert({ value: i });
    }

    FOR doc IN test 
      COLLECT AGGREGATE min = MIN(doc.value), max = MAX(doc.value) 
      RETURN { min, max }

Without the optimization, the database servers will either send the entire documents or a projection of each document (containing just each document's "value" attribute back) to the coordinator. But then each shard would still have to send 100k values on average.

With a local cluster that has 2 database servers and runs them on the same host as the coordinator, this simple query is sped up by a factor of 2 to 3 when the optimization is applied. In a "real" setup the speedup will be even higher because then there will be additional network roundtrips between the cluster nodes. And in reality documents tend to contains more data and collections tend to have more documents. If this is the case, then the speedup will be even higher.