Spill to Disk


In the case of memory intensive operations, Presto 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 Presto.

Properties related to spilling are described in Spilling Properties.

Memory Management and Spill

By default, Presto kills queries if the memory requested by the query execution exceeds session properties query_max_memory or 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 are 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.

In order to achieve more consistent query performance while still allowing larger queries to take advantage of spill-to-disk, experimental.query-limit-spill-enabled can be set to true. This property will trigger spilling for a query whenever its memory usage exceeds the per-node total memory limit, even if the memory pool is not full. See Spilling Properties for more information.

Please note that enabling spill-to-disk does not guarantee execution of all memory intensive queries. There are some memory-intensive operations that do not support spilling yet. It is also 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-total-memory-per-node size. If query.max-total-memory-per-node is large compared to the total memory available on the node, then the general memory pool may not have enough memory to run larger queries. If spill is enabled, then this will cause excessive spilling for queries that consume large amounts of memory per node. These queries would finish much quicker if spill were disabled because they would execute in the reserved pool. However, doing so could also significantly reduce cluster concurrency. In such a situation we recommend disabling the reserved memory pool via the 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 spiller-spill-path in Spilling Properties).

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.

Presto treats spill paths as independent disks (see JBOD), so there is no need to use RAID for spill.

Spill Compression

When spill compression is enabled (spill-compression-enabled property in Spilling Properties), spilled pages will be compressed using the same implementation as exchange compression when they are sufficiently compressible. Enabling this feature can reduce the amount of disk IO at the cost of extra CPU load to compress and decompress spilled pages.

Spill Encryption

When spill encryption is enabled (spill-encryption-enabled property in Spilling Properties), spill contents will be encrypted with a randomly generated (per spill file) secret key. Enabling this will decrease the performance of spilling to disk but can protect spilled data from being recovered from the files written to disk.

Note: Some distributions of Java ship with policy files that limit the strength of the cryptographic keys that can be used. Spill encryption uses 256-bit AES keys and may require Unlimited Strength JCE policy files to work correctly.

Supported Operations

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 Task Properties).

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 when memory is available.


Window functions perform an operation over groups of rows and return one value per-row. If the number of rows in your window is large, a significant amount of memory may be needed. When spill-to-disk is enabled, if there is not enough memory, then intermediate results are written to disk and read back as each window is processed. If a single window is too large, the query can still run out of memory.

Order Bys

Order by can use a lot of memory when there are many rows that need to be sorted. When spill-to-disk is enabled, if there is not enough memory, then sorted rows are written to disk and then later merged back together in memory.