Spill to Disk
In the case of memory intensive operations, openLooKeng allows offloading intermediate operation results to disk. The goal of this mechanism is to enable execution of queries that require amounts of memory exceeding per query or per node limits.
The mechanism is similar to OS level page swapping. However, it is implemented on the application level to address specific needs of openLooKeng.
Properties related to spilling are described in
Memory Management and Spill
By default, openLooKeng kills queries if the memory requested by the query execution exceeds session properties
query_max_memory_per_node. This mechanism ensures fairness in allocation of memory to queries and prevents deadlock caused by memory allocation. It is efficient when there is a lot of small queries in the cluster, but leads to killing large queries that don't stay within the limits.
To overcome this inefficiency, the concept of revocable memory was introduced. A query can request memory that does not count toward the limits, but this memory can be revoked by the memory manager at any time. When memory is revoked, the query runner spills intermediate data from memory to disk and continues to process it later.
In practice, when the cluster is idle, and all memory is available, a memory intensive query may use all of the memory in the cluster. On the other hand, when the cluster does not have much free memory, the same query may be forced to use disk as storage for intermediate data. A query that is forced to spill to disk may have a longer execution time by orders of magnitude than a query that runs completely in memory.
Please note that enabling spill-to-disk does not guarantee execution of all memory intensive queries. It is still possible that the query runner will fail to divide intermediate data into chunks small enough that every chunk fits into memory, leading to
Out of memory errors while loading the data from disk.
Revocable memory and reserved pool
Both reserved memory pool and revocable memory are designed to cope with low memory conditions. When user memory pool is exhausted then a single query will be promoted to a reserved pool. In such case only that query is allowed to progress thus reducing cluster concurrency. Revocable memory will try to prevent that by triggering spill. Reserved pool is of
query_max_memory_per_node size. This means that when
query_max_memory_per_node is large then user memory pool might be much smaller than
query_max_memory_per_node. This will cause excessive spilling for queries that consume large amounts of memory per node. Such queries could finish much quicker when spill is disabled because they execute in reserved pool. In such situations we recommend to disable reserved memory pool via
experimental.reserved-pool-enabled config property.
Spill Disk Space
Spilling intermediate results to disk and retrieving them back is expensive in terms of IO operations. Thus, queries that use spill likely become throttled by disk. To increase query performance it is recommended to provide multiple paths on separate local devices for spill (property
The system drive should not be used for spilling, especially not to the drive where the JVM is running and writing logs. Doing so may lead to cluster instability. Additionally, it is recommended to monitor the disk saturation of the configured spill paths.
openLooKeng treats spill paths as independent disks (see JBOD), so there is no need to use RAID for spill.
When spill compression is enabled (
spill-compression-enabled property in
tuning-spilling), spilled pages will be compressed before being written to dis. Enabling this feature can reduce disk IO at the cost of extra CPU load to compress and decompress
When spill encryption is enabled (
spill-encryption-enabled property in
tuning-spilling), spill contents will be encrypted with a randomly generated (per spill file) secret key.
Enabling this will increase CPU load and reduce throughput of spilling to disk, but can protect spilled data from being recovered from spill files. Consider reducing the value of
experimental.memory-revoking-threshold when spill encryption is enabled to account for the increase in latency of spilling.
Not all operations support spilling to disk, and each handles spilling differently. Currently, the mechanism is implemented for the following operations.
During the join operation, one of the tables being joined is stored in memory. This table is called the build table. The rows from the other table stream through and are passed onto the next operation if they match rows in the build table. The most memory-intensive part of the join is this build table.
When the task concurrency is greater than one, the build table is partitioned. The number of partitions is equal to the value of the
task.concurrency configuration parameter (see
When the build table is partitioned, the spill-to-disk mechanism can decrease the peak memory usage needed by the join operation. When a query approaches the memory limit, a subset of the partitions of the build table gets spilled to disk, along with rows from the other table that fall into those same partitions. The number of partitions that get spilled influences the amount of disk space needed. Afterward, the spilled partitions are read back one-by-one to finish the join operation.
With this mechanism, the peak memory used by the join operator can be decreased to the size of the largest build table partition. Assuming no data skew, this will be
1 / task.concurrency times the size of the whole build table.
Aggregation functions perform an operation on a group of values and return one value. If the number of groups you're aggregating over is large, a significant amount of memory may be needed. When spill-to-disk is enabled, if there is not enough memory, intermediate cumulated aggregation results are written to disk. They are loaded back and merged with a lower memory footprint.
If your trying to sort a larger amount of data, a significant amount of memory may be needed. When spill to disk for order by is enabled, if there is not enough memory, intemediate sorted results are written to disk. They are loaded back and merged with a lower memory footprint.
Window Functions perform an operators over a window of rows and return one value for each row. If this window of rows is large, a significant amount of memory may be needed. When spill to disk for window functions is enabled, if there is not enough memory, intemediate sorted results are written to disk. They are loaded back and merged when memory is available. There is a current limitation that spill will not work in all cases such as when a single window is very large.