Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/ldbc/ldbc_snb_interactive_v1_impls

Reference implementations for LDBC Social Network Benchmark's Interactive workload.
https://github.com/ldbc/ldbc_snb_interactive_v1_impls

snb

Last synced: 2 days ago
JSON representation

Reference implementations for LDBC Social Network Benchmark's Interactive workload.

Host: GitHub
URL: https://github.com/ldbc/ldbc_snb_interactive_v1_impls
Owner: ldbc
License: apache-2.0
Created: 2014-06-11T18:25:34.000Z (over 10 years ago)
Default Branch: main
Last Pushed: 2024-10-31T10:56:37.000Z (13 days ago)
Last Synced: 2024-10-31T11:30:26.077Z (13 days ago)
Topics: snb
Language: Java
Homepage: https://ldbcouncil.org/benchmarks/snb-interactive
Size: 12.5 MB
Stars: 98
Watchers: 19
Forks: 85
Open Issues: 4
Metadata Files:
- Readme: README.md
- License: LICENSE.txt

Awesome Lists containing this project

README

![LDBC logo](ldbc-logo.png)
# LDBC SNB Interactive v1 workload implementations

[![Build Status](https://circleci.com/gh/ldbc/ldbc_snb_interactive_v1_impls.svg?style=svg)](https://circleci.com/gh/ldbc/ldbc_snb_interactive_v1_impls)

Reference implementations of the LDBC Social Network Benchmark's Interactive workload ([paper](https://homepages.cwi.nl/~boncz/snb-challenge/snb-sigmod.pdf), [specification on GitHub pages](https://ldbcouncil.org/ldbc_snb_docs/), [specification on arXiv](https://arxiv.org/pdf/2001.02299.pdf)).

To get started with the LDBC SNB benchmarks, check out our introductory presentation: [The LDBC Social Network Benchmark](https://docs.google.com/presentation/d/1NilxSrKQnFq4WzWMY2-OodZQ2TEksKzKBmgB20C_0Nw/) ([PDF](https://ldbcouncil.org/docs/presentations/ldbc-snb-2021-12.pdf)).

## Notes

:warning: Please keep in mind the following when using this repository.

* The goal of the implementations in this repository is to serve as **reference implementations** which other implementations can cross-validated against. Therefore, our primary objective was readability and not absolute performance when formulating the queries.

* The default workload contains updates which are persisted in the database. Therefore, **the database needs to be reloaded or restored from backup before each run**. Use the provided `scripts/backup-database.sh` and `scripts/restore-database.sh` scripts to achieve this.

* We expect most systems-under-test to use multi-threaded execution for their benchmark runs. **To allow running the updates on multiple threads, the update stream files need to be partitioned accordingly by the generator.** We have [pre-generated](#benchmark-data-sets) these for frequent partition numbers (1, 2, ..., 1024 and 24, 48, 96, ..., 768) and scale factors up to 1000.

## Implementations

We provide three reference implementations:

* [Neo4j (Cypher) implementation](cypher/README.md)
* [PostgreSQL (SQL) implementation](postgres/README.md)
* [GraphDB (SPARQL) implementation](graphdb/README.md)

Additional implementations:

* [DuckDB (SQL) implementation](duckdb/README.md)
* [TigerGraph (GSQL) implementation](tigergraph/README.md)
* [Umbra (SQL) implementation](umbra/README.md)

For detailed instructions, consult the READMEs of the projects.

To build a subset of the projects, use Maven profiles, e.g. to build the reference implementations, run:

```bash
mvn clean package -DskipTests -Pcypher,postgres
```

## User's guide

### Building the project

This project uses Java 11.

To build the project, run:

```bash
scripts/build.sh
```

### Inputs

The benchmark framework relies on the following inputs produced by the [SNB Datagen](https://github.com/ldbc/ldbc_snb_datagen_hadoop/):

* **Initial data set:** the SNB graph in CSV format (`social_network/{static,dynamic}`)
* **Update streams:** the input for the update operations (`social_network/updateStream_*.csv`)
* **Substitution parameters:** the input parameters for the complex queries. It is produced by the Datagen (`substitution_parameters/`)

### Driver modes

For each implementation, it is possible to perform to perform the run in one of the [SNB driver's](https://github.com/ldbc/ldbc_snb_interactive_v1_driver) three modes: create validation parameters, validate, and benchmark.
The execution in all three modes should be started after the initial data set was loaded into the system under test.

1. Create validation parameters with the `driver/create-validation-parameters.sh` script.

2. Validate against an existing reference output (called "validation parameters") with the `driver/validate.sh` script.

* **Input:**
* The query substitution parameters are taken from the validation parameters file (e.g. `validation_params.csv`) file set in the `validate_database` configuration property.
* The update operations are also based on the content of the validation parameters file.
* **Output:**
* The validation either passes of fails.
* The per query results of the validation are printed to the console.
* If the validation failed, the results are saved to the `validation_params-failed-expected.json` and `validation_params-failed-actual.json` files.
* **Parallelism:** The execution must be single-threaded to ensure a deterministic order of operations.

3. Run the benchmark with the `driver/benchmark.sh` script.

* **Inputs:**
* The query substitution parameters are taken from the directory set in `ldbc.snb.interactive.parameters_dir` configuration property.
* The update streams are the `updateStream_*_{forum,person}.csv` files from the location set in the `ldbc.snb.interactive.updates_dir` configuration property.
* To get *2n* write threads, the framework requires *n* `updateStream_*_forum.csv` and *n* `updateStream_*_person.csv` files.
* If you are generating the data sets from scratch, set `ldbc.snb.datagen.serializer.numUpdatePartitions` to *n* in the [data generator](https://github.com/ldbc/ldbc_snb_datagen_hadoop) to get produce these.
* The goal of the benchmark is the achieve the best (lowest possible) `time_compression_ratio` value while ensuring that the 95% on-time requirement is kept (i.e. 95% of the queries can be started within 1 second of their scheduled time). If your benchmark run returns "failed schedule audit", increase this number (which lowers the time compression rate) until it passes.
* Set the `thread_count` property to the size of the thread pool for read operations.
* For audited benchmarks, ensure that the `warmup` and `operation_count` properties are set so that the warmup and benchmark phases last for 30+ minutes and 2+ hours, respectively.
* **Output:**
* Passed or failed the "schedule audit" (the 95% on-time requirement).
* The throughput achieved in the run (operations/second).
* The detailed results of the benchmark are printed to the console and saved in the `results/` directory.
* **Parallelism:** Multi-threaded execution is recommended to achieve the best result.

For more details on validating and benchmarking, visit the [driver's documentation](https://github.com/ldbc/ldbc_snb_interactive_v1_driver/tree/main/docs).

## Developer's guide

To create a new implementation, it is recommended to use one of the existing ones: the Neo4j implementation for graph database management systems and the PostgreSQL implementation for RDBMSs.

The implementation process looks roughly as follows:

1. Create a bulk loader which loads the initial data set to the database.
2. Implement the complex and short reads queries (22 in total).
3. Implement the 7 update queries.
4. Test the implementation against the reference implementations using various scale factors.
5. Optimize the implementation.

## Data sets

### Benchmark data sets

To generate the benchmark data sets, use the [Hadoop-based LDBC SNB Datagen](https://github.com/ldbc/ldbc_snb_datagen_hadoop/releases/tag/v1.0.0).

The key configurations are the following:

* `ldbc.snb.datagen.generator.scaleFactor`: set this to `snb.interactive.${SCALE_FACTOR}` where `${SCALE_FACTOR}` is the desired scale factor
* `ldbc.snb.datagen.serializer.numUpdatePartitions`: set this to the number of write threads used in the benchmark runs
* serializers: set these to the required format, e.g. the ones starting with `CsvMergeForeign` or `CsvComposite`
* `ldbc.snb.datagen.serializer.dynamicActivitySerializer`
* `ldbc.snb.datagen.serializer.dynamicPersonSerializer`
* `ldbc.snb.datagen.serializer.staticSerializer`

### Pre-generated data sets

Producing large-scale data sets requires non-trivial amounts of memory and computing resources (e.g. SF100 requires 24GB memory and takes about 4 hours to generate on a single machine).
To mitigate this, we have pregenerated data sets using 9 different serializers and the update streams using 17 different partition numbers:

* Serializers: csv_basic, csv_basic-longdateformatter, csv_composite, csv_composite-longdateformatter, csv_composite_merge_foreign, csv_composite_merge_foreign-longdateformatter, csv_merge_foreign, csv_merge_foreign-longdateformatter, ttl
* Partition numbers: 2^k (1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024) and 6×2^k (24, 48, 96, 192, 384, 768).

The data sets are available at the [SURF/CWI data repository](https://repository.surfsara.nl/datasets/cwi/ldbc-snb-interactive-v1-datagen-v100). We also provide [direct links](https://ldbcouncil.org/data-sets-surf-repository/snb-interactive-v1-datagen-v100) and a [download script](https://ldbcouncil.org/data-sets-surf-repository/#usage) (which stages the data sets from tape storage if they are not immediately available).

### Validation parameters

We provide [**validation parameters for SF0.1 to SF10**](https://pub-383410a98aef4cb686f0c7601eddd25f.r2.dev/interactive-v1/validation_params-interactive-v1.0.0-sf0.1-to-sf10.tar.zst). These were produced using the Neo4j reference implementation.

### Test data set

Small test data sets are placed in the `cypher/test-data/` directory for Neo4j and in the `postgres/test-data/` for the SQL systems.

To generate a data set with the same characteristics, see the [documentation on generating the test data set](test-data).

## Preparing for an audited run

Implementations of the Interactive workload can be audited by a certified LDBC auditor.
The [Auditing Policies chapter](http://ldbcouncil.org/ldbc_snb_docs/ldbc-snb-specification.pdf#chapter.9) of the specification describes the auditing process and the required artifacts.
If you are considering commissioning an LDBC SNB audit, please study the [auditing process document](https://ldbcouncil.org/docs/ldbc-snb-auditing-process.pdf) and the [audit questionnaire](snb-interactive-audit-questionnaire.md).

### Determining the best TCR

1. Select a scale factor and configure the `driver/benchmark.properties` file as described in the [Driver modes](#driver-modes) section.
2. Load the data set with `scripts/load-in-one-step.sh`.
3. Create a backup with `scripts/backup-database.sh`.
4. Run the `driver/determine-best-tcr.sh`.
5. Once the "best TCR" value has been determined, test it with a full workload (at least 0.5h for warmup operation and at least 2h of benchmark time), and make further adjustments if necessary.

### Recommendations

We have a few recommendations for creating audited implementations. (These are not requirements – implementations are allowed to deviate from these recommendations.)

* The implementation should target a popular Linux distribution (e.g. Ubuntu LTS, CentOS, Fedora).
* Use a containerized setup, where the DBMS is running in a Docker container.
* Instead of a specific hardware, target a cloud virtual machine instance (e.g. AWS `r5d.12xlarge`). Both bare-metal and regular instances can be used for audited runs.