Hive connector storage caching#
Примечание
Ниже приведена оригинальная документация Trino. Скоро мы ее переведем на русский язык и дополним полезными примерами.
Querying object storage with the Hive коннектор is a very common use case for Trino. It often involves the transfer of large amounts of data. The objects are retrieved from HDFS, or any other supported object storage, by multiple workers and processed on these workers. Repeated queries with different parameters, or even different queries from different users, often access, and therefore transfer, the same objects.
Benefits#
Enabling caching can result in significant benefits:
Reduced load on object storage
Every retrieved and cached object avoids repeated retrieval from the storage in subsequent queries. As a result the object storage system does not have to provide the object again and again.
For example, if your query accesses 100MB of objects from the storage, the first time the query runs 100MB are downloaded and cached. Any following query uses these objects. If your users run another 100 queries accessing the same objects, your storage system does not have to do any significant work. Without caching it has to provide the same objects again and again, resulting in 10GB of total storage to serve.
This reduced load on the object storage can also impact the sizing, and therefore the cost, of the object storage system.
Increased query performance
Caching can provide significant performance benefits, by avoiding the repeated network transfers and instead accessing copies of the objects from a local cache. Performance gains are more significant if the performance of directly accessing the object storage is low compared to accessing the cache.
For example, if you access object storage in a different network, different data center or even different cloud-provider region query performance is slow. Adding caching using fast, local storage has a significant impact and makes your queries much faster.
On the other hand, if your object storage is already running at very high performance for I/O and network access, and your local cache storage is at similar speeds, or even slower, performance benefits can be minimal.
Reduced query costs
A result of the reduced load on the object storage, mentioned earlier, is significantly reduced network traffic. Network traffic however is a considerable cost factor in an setup, specifically also when hosted in public cloud provider systems.
Architecture#
Caching can operate in two modes. The async mode provides the queried data directly and caches any objects asynchronously afterwards. Async is the default and recommended mode. The query doesn’t pay the cost of warming up the cache. The cache is populated in the background and the query bypasses the cache if the cache is not already populated. Any following queries requesting the cached objects are served directly from the cache.
The other mode is a read-through cache. In this mode, if an object is not found in the cache, it is read from the storage, placed in the cache, and then provided to the requesting query. In read-through mode, the query always reads from cache and must wait for the cache to be populated.
In both modes, objects are cached on local storage of each worker. Workers can request cached objects from other workers to avoid requests from the object storage.
The cache chunks are 1MB in size and are well suited for ORC or Parquet file formats.
Конфигурация#
The caching feature is part of the Hive коннектор and can be activated in the catalog properties file:
connector.name=hive
hive.cache.enabled=true
hive.cache.location=/opt/hive-cache
The cache operates on the coordinator and all workers accessing the object storage. The used networking ports for the managing BookKeeper and the data transfer, by default 8898 and 8899, need to be available.
To use caching on multiple catalogs, you need to configure different caching directories and different BookKeeper and data-transfer ports.
Property |
Description |
Default |
|---|---|---|
|
Toggle to enable or disable caching |
|
|
Required directory location to use for the cache storage on each worker.
Separate multiple directories, which can be mount points for separate
drives, with commas. More tips can be found in the recommendations. Example:
|
|
|
The TCP/IP port used to transfer data managed by the cache. |
|
|
The TCP/IP port used by the BookKeeper managing the cache. |
|
|
Operational mode for the cache as described earlier in the architecture
section. |
|
|
Time to live for objects in the cache. Objects, which have not been requested for the TTL value, are removed from the cache. |
|
|
Percentage of disk space used for cached data. |
80 |
Recommendations#
The speed of the local cache storage is crucial to the performance of the cache. The most common and cost efficient approach is to attach high performance SSD disk or equivalents. Fast cache performance can be also be achieved with a RAM disk used as in-memory.
In all cases, you should avoid using the root partition and disk of the node and instead attach at multiple dedicated storage devices for the cache on each node. The cache uses the disk up to a configurable percentage. Storage should be local on each coordinator and worker node. The directory needs to exist before Trino starts. We recommend using multiple devices to improve performance of the cache.
The capacity of the attached storage devices should be about 20-30% larger than the size of the queried object storage workload. For example, your current query workload typically accesses partitions in your HDFS storage that encapsulate data for the last 3 months. The overall size of these partitions is currently at 1TB. As a result your cache drives have to have a total capacity of 1.2 TB or more.
Your deployment method for Trino decides how to create the directory for caching. Typically you need to connect a fast storage system, like an SSD drive, and ensure that is it mounted on the configured path. Kubernetes, CFT and other systems allow this via volumes.
Object storage systems#
The following object storage systems are tested:
HDFS
Google Cloud Storage
Metrics#
In order to verify how caching works on your system you can take multiple approaches:
Inspect the disk usage on the cache storage drives on all nodes
Query the metrics of the caching system exposed by JMX
The implementation of the cache exposes a number of metrics via JMX. You can inspect these and other metrics directly in Trino with the JMX connector or in external tools.
Basic caching statistics for the catalog are available in the
jmx.current."rubix:catalog=<catalog_name>,name=stats" table.
The table jmx.current."rubix:catalog=<catalog_name>,type=detailed,name=stats
contains more detailed statistics.
The following example query returns the average cache hit ratio for the hive catalog:
SELECT avg(cache_hit)
FROM jmx.current."rubix:catalog=hive,name=stats"
WHERE NOT is_nan(cache_hit);
Limitations#
Caching does not support user impersonation and cannot be used with HDFS secured by Kerberos. It does not take any user-specific access rights to the object storage into account. The cached objects are simply transparent binary blobs to the caching system and full access to all content is available.