Here you have 8 more techniques for improving ingest performance with TimescaleDB: 6. If you prefer to try Timescale in AWS, you can create a free account on our platform. If you're running your PostgreSQL database in your own hardware, you can simply add the TimescaleDB extension. If getting faster ingest performance out of PostgreSQL is necessary for your applications or systems, give TimescaleDB a go. The ideal Timescale scenarios are time-centric, almost solely append-only (lots of INSERTs), and require fast ingestion of large amounts of data within small time windows. The most common uses for TimescaleDB involve storing massive amounts of data for cloud infrastructure metrics, product analytics, web analytics, IoT devices, and many use cases involving large PostgreSQL tables. TimescaleDB is built to improve query and ingest performance in PostgreSQL. Choose and Configure TimescaleDB for Better Ingest Performance See if alternative storage configurations are feasible. If you are seeing far fewer, your INSERT performance is likely to be impacted by your disk hardware. You should see at least 1000s of read IOPS and many 100s of write IOPS. Generated 10 requests in 9.00 s, 80 KiB, 1 iops, 8.89 KiB/s Min/avg/max/mdev = 18 us / 23.1 us / 35 us / 6.17 usĩ requests completed in 10.8 ms, 72 KiB written, 830 iops, 6.49 MiB/s Generated 10 requests in 9.00 s, 80 KiB, 1 iops, 8.88 KiB/s (hfs /dev/disk1 930.7 GiB) ioping statistics -ĩ requests completed in 208 us, 72 KiB read, 43.3 k iops, 338.0 MiB/s One thing to do is check your disk IOPS using the ioping command. And because when you are inserting rows, the data is durably stored to the write-ahead log (WAL) before the transaction completes, slow disks can impact insert performance. Sometimes developers deploy their database in environments with slower disks, whether due to poorly-performing HDD, remote SANs, or other types of configurations. While this is a more advanced optimization that isn't always needed, if your disk becomes a bottleneck, you can further increase throughput by using a separate disk (tablespace) for the database's write-ahead log (WAL) and data. If a UNIQUE constraint is otherwise defined, that insert can necessitate an index lookup to determine if the row already exists, which will adversely impact the speed of your INSERT. Yet, many use cases-including common monitoring or time-series applications-don’t require them, as each event or sensor reading can simply be logged as a separate event by inserting it at the tail of a hypertable's current chunk during write time. Avoid unnecessary UNIQUE keysĭevelopers are often trained to specify primary keys in database tables, and many ORMs love them. Consider if you can denormalize your data-we sometimes see pretty extreme use of FK constraints, done from a sense of “elegance” rather than engineering tradeoffs. When you have an FK constraint, every INSERT will typically then need to read from your referenced table, which can degrade performance. Sometimes it's necessary to build foreign keys (FK) from one table to other relational tables. Since every system is different, there aren’t any hard and fast rules or “magic number” of indexes-just be reasonable. Check the number of indexes you’ve defined on your table (use the psql command \d table_name), and determine whether their potential query benefits outweigh the storage and insert overhead. Incrementally maintaining indexes with each new row requires additional work. Having the right indexes can speed up your queries, but they’re not a silver bullet. Here are some best practices for improving ingest performance in vanilla PostgreSQL: 1. Don't know what Timescale is? Read this article.
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