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Big Graph Data - Ingesting 17 billion triples into TerminusDB

Smaller Is Better: Ultra-Compact Graph Representations for a Big Graph

Recently at TerminusDB, at the behest of an active community member, we decided to do an ingest of the OpenAlex Authors collection to create a big graph. This is a pretty big dataset. We found that after the ingest, not only did we have a database with 17 billion triples, but in comparison, our database is smaller than others (only 212GB as compared to 280GB), even though it is much better indexed. It also has the most compact triple store representation we are aware of, coming in at less than 14 bytes per triple for the tested dataset.

You can search starting from subject, object, or predicate, in any direction, and get results quickly with an extremely low memory footprint, due to the utility of succinct data structures.

Big Data Is Not Always Needed

When TerminusDB was first getting started, we did a project loading public information about the Polish economy into a big graph. The project had a lot of custom code that would merge information into a single compressed representation of a graph which could then be efficiently searched. The dataset was around 3 billion triples.

This ingestion was a custom solution. It had to be ingested as a batch and could not be updated to correct information without starting over from scratch, and took a very long time (over a day) to ingest on a fairly large parallel computer with custom ingestion code.

Since that time, we focused on making TerminusDB more user-friendly, making it easier to get started by loading JSON documents that are readable, and including schema checking to ensure that we don’t suffer from garbage data in the first place.

Since most databases that are in active use are less than 2GB in size, this was probably the right decision. Very many real-world use cases do not require enormous data sets and ease of use is more important.

But I Want a Gigantic Knowledge Graph!

However, sometimes, as with the original Polish economy use case, enormous datasets are precisely what we want. Recently one active member of TerminusDB’s community asked us if we could load the Authors’ collection from the OpenAlex dataset, which incorporates an enormous amount of information on scientific publishing.

TerminusDB’s internals are designed to store very compact representations of graphs, so we figured (with some back-of-the-napkin calculations) that it might be possible to build a significant subset of OpenAlex into a single knowledge graph. . . with a few changes to TerminusDB to facilitate doing so without custom ingestion. Our ingestion of OpenAlex logic is written in Python.

Parallelizing Ingest

To parallelize the ingest, we created 500 separate databases, each responsible for one chunk of the data input. We segmented the data input into 500 pieces. We then started 64 processes for ingest for one database-chunk pair for each processor on a large 64-processor, 500GB RAM machine. Every time one was completed, we’d start a new process. This way, all processors were saturated with an ingest process until completion.

The fragment size for ingest was calculated by the back-of-the-napkin calculation that an ingest requires about 10x the memory of the final database, and so we cut the input data into sizes that would fill memory when saturating all processors. It would eventually be nice if TerminusDB could just do this for you with its own calculations, but this works well enough for now.

The main part of the ingest script is the following simple Python code:

					def ingest_json(args):
    start = time.time()
    filename = args[0]
    number = args[1]
    schema = args[2]
    db_name = f"openalex_{number}"
    db = f'admin/{db_name}'
    with open(f"log/{db_name}.log", 'w') as f:
        subprocess.run(f"{TERMINUSDB_COMMAND} doc insert {db} -g schema --full-replace < {schema}", shell=True, stdout=f, stderr=f)
        subprocess.run(f'{TERMINUSDB_COMMAND} doc insert {db} < {filename}', shell=True, stdout=f, stderr=f)
        end_insert = time.time() - start
        f.write(f"\n\nEND TIME: {end_insert}\n")

This fires off a terminusdb doc insert command for a given database, which we can form from an argument that we pass. We can fire off just the right number of these (for as many processors as we have) with:

					    with Pool(args.threads) as p:
        # Ingest JSON
        p.map(ingest_json, args_process)

For our ingest, this process took about 7 hours to complete.

Concatenating the Databases

To concatenate these 500 databases, we needed a new approach to building a single union of a set of databases. We decided that we would write a new concatenate operation (which we added to TerminusDB) that could read any number of base layers and concatenate them into a single new base layer.

TerminusDB is immutable, so we perform updates by adding new layers that include changes to the database (delta-encoding). The first such layer is called the base layer.

Merging base layers is less complicated as there is only one layer to account for. Furthermore, one can always acquire a base layer by first performing a squash on a layer to obtain a single new base layer, if the database has a history of revisions. We figured requiring base layers in concatenate was a reasonable compromise for the interface.

Since we have so many databases, we don’t want to have to specify them all on the command line (in fact we might not even be able to), so we take them on standard input.

The command is of the form:

					$ echo "admin/db1 admin/db2 ... admin/dbn" | terminusdb concat admin/final

Where the databases are a space-separated list of all of the input databases. That’s all there is to it!

Sparing Use of Memory

Doing this 500-database-concatenate requires some careful attention to memory. TerminusDB’s memory overhead for a database is quite low, despite having a highly indexed data structure allowing traversal in every direction in the graph, due to the use of succinct data structures.

Our final complete ingest, a big graph that represents 17 billion triples, is only 13.57 bytes per triple!

To give an idea of how this ranks next to other graph databases, here are some comparisons from here, with the caveat that they are working with a different dataset:

DatabaseBytes Per Triple
Jena LTJ168.84
RDF-3X 85.73

This of course isn’t the final word either, we have identified some approaches along the way that might shrink this further, but it’s impressive nonetheless! Simply maintaining a table of triples of 64-bit identifiers would be significantly larger.

The process of building our indexing structures, however, was requiring significantly more memory than the final index. So we spent a bit of time trying to make sure that we could do nearly everything by streaming the input, lowering the amount of working memory required to the absolute minimum.


We rewrote much of our layer writing code to take all the inputs as streams. Base layers are composed of a number of different segments, including (node and value) dictionaries, and adjacency lists. These are all ordered, meaning that it’s possible to do the second half of a merge sort (the “conquer” part of “divide and conquer”) in order to merge them in sorted order.

To make the comparison of all of the next 500 elements fast, we use a binary heap. This is initialized with the first 500 elements of each stream, after which we pop off the least element and read another element from that stream.


To build the indexes that allow quick lookup backward from objects to subject-predicate pairs, requires that we do an additional sort.

As it turned out, doing a parallel sort over the data using the Tokyo routines in Rust was just a bit too much to stay under 500GB of memory.

Instead, we had to chunk out pieces to sort, a bit at a time, and recombine.

A Giant Concatenation

The final layer is only around 212GB, so fits very comfortably in a 500GB machine. With GraphQL you can query this data quickly. Being able to fit so much into a single machine means you can get graph performance which would simply be impossible with a sharding approach.

The concatenate step takes around 5 hours. So within 12 hours, we can build a ~200GB database from JSON files to queryable layers.

The entire process, when mapped out, looks something like this:

         JSON1     JSON2   JSON3    ....
           |         |       |       |
          DB1       DB2     DB3     DBN
            \        |      /       /
             \       |     /       /
              \      |    /       /
               \     |   /  _____/
               concatenate process
concatenate nodes + concatenate predicates + concatenate values
        \         |              /
         \        |             /
          \       |            /
           \      |           /
            \     |          /
             concatenate triples
              build indexes
              write to disk

The Future

One of our central theses about graphs is that, due to the poor memory locality of graphs and graph search, it’s best if you can fit everything into memory. That’s why we haven’t bothered with paging approaches to our graphs (as opposed to our vector database). The better the memory performance, the better the performance overall. As soon as you’re hitting network or disk to traverse links, you’re getting many orders of magnitude worse performance.

If you want more in a graph, you should either:

  1. Segment your graph logically – keep separate chunks in separate data products.
  2. Reduce the memory overhead.

The first solution is really about data design. If you can break a data product into separate data product components, then you can reduce the total amount you need to have on a single machine. These logical segmentations can’t be done automatically but are very important.

The second solution can be more automatic. TerminusDB is excellent in terms of memory performance as it stands. But we’d like to be able to increase the amount we can fit in a single machine, even above what we have now.

One thing that would reduce memory significantly is if we did not index all backward links, but only those we know will be used. This would require adding explicit indexing (or explicit non-indexing) of predicates in the schema design. We estimate this could provide savings of 10%-25% of memory.

Other alternatives include using alternative indexing strategies that are also succinct. Perhaps an FM-index or a k^d-tree. Whether these will be smaller in practice would require some experimentation.

In any case, we’ll keep plumbing the limits with our motto in hand: Smaller is better for a big graph.


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