We are faced with this question: “What’s the ingestion rate of Postgres-XL?”, and I realised I don’t have a very good answer to that. Since recently we made some good improvements in this area, I was curious to know too. Well, I decided to benchmark.
Hardware and Software
For the tests, I used a Postgres-XL cluster running on EC2 instances. Since COPY has to go through the coordinator, it seemed reasonable to use a compute-optimised c3.8xlarge instance for running coordinator. Similarly, for datanodes, storage-optimised i2.xlarge instances are more appropriate. Both these instances have attached SSD disks, though i2.xlarge instance has more storage than the c3.8xlarge instance.
So the next question was how to generate data for the test? We’d used TPC-H benchmark for previous performance tests and it had everything that I needed for this simple, focused test. I just picked up one table from the TPC-H benchmark, the lineitem table. It’s a very wide table, with many columns and a wide VARCHAR column. The ingestion benchmarks I’d seen earlier used a very narrow table, so for fairness I also stripped away a few columns. The resulting table was still much wider, each row being about 90 bytes, including tuple headers.
CREATE TABLE lineitem ( l_orderkey BIGINT NOT NULL, l_partkey BIGINT NOT NULL, l_suppkey BIGINT NOT NULL, l_linenumber BIGINT NOT NULL, l_shipdate DATE NOT NULL, l_receiptdate DATE NOT NULL, l_shipinstruct CHAR(25) NOT NULL ) DISTRIBUTE BY HASH (l_orderkey);
TPC-H benchmark comes with a data generation tool called dbgen. You can generate data only for a specific table, of required size, and split it into multiple chunks if needed. So I created sample data of 3 billion rows, split into 24 chunks. Since the c3.8xlarge instance has two SSDs attached to it, the data files were split onto the disks to utilise full IO bandwidth at the coordinator side.
for i in `seq 1 12`; do dbgen -C 24 -S $i -T L -s 500 -o | cut -d '|' -f 1,2,3,4,11,13,14 > /data1/pgxl/lineitem_part_$i.csv& done
for i in `seq 13 24`; do dbgen -C 24 -S $i -T L -s 500 -o | cut -d '|' -f 1,2,3,4,11,13,14 > /data2/pgxl/lineitem_part_$i.csv& done
We ran two benchmarks. First we used a single coordinator to run all 24 COPY processes.
for i in `seq 1 12`; do time psql -c "COPY lineitem FROM '/data1/pgxl/lineitem_part_$i.csv' WITH (FORMAT CSV, DELIMITER '|')"& done
for i in `seq 13 24`; do time psql -c "COPY lineitem FROM '/data2/pgxl/lineitem_part_$i.csv' WITH (FORMAT CSV, DELIMITER '|')"& done
The total size of the table upon load was approximately 283GB. Same tests were repeated on UNLOGGED tables to see if the WAL becomes a bottleneck. The results are very interesting and clearly show that Postgres-XL is able to ingest data at a very high speed.
So we could load at a rate of 6.7M rows/sec or 683 MB/sec. That’s an impressive rate to load data into a relational database. This also matches performance numbers shown by other PostgreSQL based scale-out solutions, on a comparable hardware.
To see if more performance can be extracted from the datanodes and check if the coordinator is a bottleneck, we added another coordinator to the cluster. This time the COPY processes were equally split between two coordinators, such that each coordinator runs 12 concurrent COPY processes. The results showed a good 34% speed up in load time, loading 3 billion rows in little over 5 minutes. The run with unlogged table topped 9M rows/sec. When measured in terms of number of bytes/hr, this translates into more than 3TB/hr of ingestion rate.
It’s important to note that adding one more coordinator to the cluster improved the ingestion rate. So Postgres-XL’s multi-coordinator model helps in more than one way.
Just in case you’re curious, I also performed a simple aggregate query on the entire table and it finished in 36 seconds. That’s cool 82M rows/sec read rate or 30TB/hr of collective scan rate. (Note: much of the data may have been cached at the datanodes).
postgres=# select count(*) from lineitem ; count ------------ 3000028242 (1 row) Time: 36252.452 ms
Note: To check if the COPY rate can be sustained over a larger dataset, we repeated the test with a regular table 3 times, without truncating the table, and saw no drop in the performance. The table size at the end of the 3rd run was over 800GB, which is more than the collective RAM on the datanodes.
Even though Postgres-XL didn’t really hit 10M rows/sec in these tests, it looks like only a matter of tweaking a few knobs, doesn’t it? Stay tuned for my next blog as I continue to perform some more tests to see if we can really hit that magic figure of 10M rows/sec.
- Postgres-XL topped 9M rows/sec upload rate. We would like to test further to check if adding more datanodes and/or coordinators can further improve the load rate.
- Postgres-XL’s ability to support multiple coordinators is very useful for ingesting a large amount of data.
- The results show a nearly 1GB/sec ingestion rate.
- Vladimir asked how many datanodes were used in these tests. The tests were run on a cluster with 16 datanodes, running on i2.xlarge EC2 instances. In addition, we used 1 or 2 coordinators (see above) running on c3.8xlarge instances.