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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsFacebook made its warehouse faster by changing both query execution and the way data was stored and read. Its Presto engine pipelined SQL work instead of waiting for successive disk-mediated MapReduce stages to finish, while customized ORC storage and a purpose-built reader reduced the data and decoding work needed for selective queries. Facebook reported large gains in its own systems between 2013 and 2015, but the results depended on workload and test conditions.
Why Facebook needed faster warehouse queries
Facebook’s data warehouse ran on large Hadoop and HDFS clusters. Hive and MapReduce provided reliable, large-scale processing, but interactive analysis had a different requirement: analysts needed answers with lower latency, not just the ability to process large volumes of data. Facebook’s Data Infrastructure team started Presto in fall 2012; the company said its first production system was running in early 2013 and its company-wide rollout was complete by spring that year.
The scale helps explain the engineering problem. In its November 2013 account, Facebook said the warehouse held more than 300 petabytes and Presto processed more than 30,000 queries—covering one petabyte of data—daily, with more than 1,000 employees using it. In April 2014, Facebook described a 300 PB warehouse receiving about 600 TB per day and growing to three times its size over the preceding year. These are company-reported figures from those dates, not current measurements.
Presto changed how query stages exchanged data
In the Hive/MapReduce path Facebook described, a query was broken into sequential stages. Tasks read inputs from disk and wrote intermediate results back to disk, so later work could wait for earlier stages and their output. Presto instead ran stages concurrently and streamed intermediate data onward as it became available. That reduced waiting and avoidable intermediate I/O; it did not mean that queries never read from disk or that the warehouse was held entirely in memory.
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| Approach | Execution model | Role in Facebook’s account |
|---|---|---|
| Hive with MapReduce | Sequential MapReduce stages read inputs and write intermediate outputs to disk. | Large transformations and warehouse table processing. |
| Presto | Distributed SQL stages run concurrently and stream data between stages. | Interactive, ad-hoc analysis with a lower-latency goal. |
Presto’s coordinator parsed, analyzed, and planned SQL, then assigned work across nodes close to data. Connectors let the engine query Hive/HDFS and other stores. Facebook presented Presto and Hive as complementary tools, not as a wholesale replacement of Hive.
Facebook’s 2013 engineering post characterized Presto as delivering 10× better CPU efficiency and latency for most of its queries compared with Hive/MapReduce. “Most” matters: this was the company’s characterization of its own queries and systems, not an independent benchmark or a result for every query.
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Facebook improved storage as well as execution
Faster query planning and scheduling could not eliminate the cost of reading and decoding warehouse data. Facebook’s 2014 account described moving from RCFile toward a customized ORCFile format. RCFile grouped rows and stored each column in a contiguous chunk, allowing a query to avoid decompressing and deserializing columns it did not use. Facebook reported average compression of 5× on a representative sample of its raw warehouse data with RCFile.
The team added column-level encodings, including run-length, dictionary, frame-of-reference, and numeric encodings. Rather than apply one encoding everywhere, it used observed column values and distinct-value thresholds to choose where dictionary encoding was worthwhile; high-entropy strings, for example, could grow rather than shrink under dictionary encoding. The team also considered character sets when choosing encodings and adjusted integer encoding. Facebook said it selected a 256 MB ORC stripe size empirically for its environment.
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Facebook reported that these storage changes raised average compression from 5× with RCFile to 8× with Facebook ORCFile across its representative data. In its tests, selective queries on Facebook ORCFile ran 3× faster than on open-source ORCFile. The company also said the format had rolled out to many tens of petabytes and reclaimed tens of petabytes of capacity; both are dated company-reported rollout claims.
Writer changes reduced the cost of producing files
Encoding and compression can save read and storage costs but make file production more expensive. Facebook changed its writer as well: replacing a red-black-tree dictionary structure with a memory-efficient hash map reduced dictionary memory footprint by 30% and improved write performance by 1.4×, according to its 2014 post. A later switch to Airlift Slice improved writer performance by a further 20–30%. After the format improvements, Facebook also lowered the Zlib compression level and reported a 20% writing-performance gain with minimal compression impact. These are measurements from Facebook’s implementation, not guaranteed gains for other systems.
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The newer reader avoided work on data a query did not need
In March 2015, Facebook described a Presto-specific reader for ORC and DWRF. The available Hive readers and Facebook’s DWRF reader did not, in combination, offer the three capabilities and type support the team wanted. The new reader addressed that gap in three ways:
- Columnar reads: It fed columns directly to Presto, rather than reading rows and reorganizing them into columns.
- Predicate pushdown: It used recorded minimum and maximum values at file, stripe, and finer granularities to skip segments that could not meet a filter.
- Lazy reads: It examined filter columns first, then read other columns only for segments with matching rows.
Predicate pushdown is most useful when stored minimum and maximum values can rule out a segment. It may be less effective for exact-match filters on high-cardinality identifiers: a segment’s range can include the requested identifier even when the identifier is absent. Lazy reads can still help in that case by postponing other-column reads until the filter has identified matching data.
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What Facebook’s speed figures do—and do not—show
Facebook’s 2015 post reported 2–4× wall-time and CPU-time speedups for its new Presto ORC reader against an older Hive-based ORC reader and an RCFile-binary reader on terabyte-scale, ZLIB-compressed tables. It also reported 4× or greater gains with lazy reads and 30× or greater with predicate pushdown on tested workloads. The post cautioned that its carefully crafted queries stressed the reader; bandwidth-bound or computation-heavy queries could see little or no improvement.
| Reported result | What Facebook compared or measured | Important boundary |
|---|---|---|
| 10× better CPU efficiency and latency for most queries | Presto versus Hive/MapReduce, as Facebook characterized results in 2013. | Company-reported result for most Facebook queries, not all queries or an independent benchmark. |
| 5× average compression, later 8× | RCFile versus Facebook ORCFile on Facebook’s representative warehouse data, reported in 2014. | Compression ratios describe that data sample and implementation. |
| 3× faster selective queries | Facebook ORCFile versus open-source ORCFile in Facebook’s 2014 tests. | Specific to the tested selective queries and files. |
| 2–4× wall-time and CPU-time speedup | New Presto ORC reader versus older Hive-based ORC and RCFile-binary readers on terabyte-scale ZLIB-compressed tables, reported in 2015. | Results vary with workload and measured metric. |
| 4×+ with lazy reads; 30×+ with predicate pushdown | Facebook’s 2015 reader workloads. | Carefully crafted queries stressed the reader; other bottlenecks could leave little or no gain. |
The 2015 account also described a benchmark using TPC-H-generated data, a 14-machine test cluster, Presto 0.89, and Impala 2.0.1. Results varied with column type, compression, and number of columns; CPU-time and wall-time comparisons could differ when a system did not use all test-machine CPUs. A multiplier without those conditions is not a sound basis for predicting how another engine or workload will perform.
When these techniques are likely to matter
The engineering lesson is a stack, not a single speed switch. Pipelining targets delay between query stages; columnar formats and selective decoding target unnecessary data work; predicate pushdown targets segments that file statistics can rule out. Which one matters most depends on where a query spends its time and how its filters match the stored data.
- If a query waits on sequential stages and intermediate writes, concurrent pipelining can reduce stage-boundary delays.
- If it reads many columns but uses few, columnar storage and lazy decoding can avoid processing irrelevant values.
- If file statistics exclude large portions of data, predicate pushdown can skip those segments; if statistics are too broad, lazy reads may still defer work on nonmatching data.
- If a query is limited by bandwidth or heavy computation rather than reader overhead, the 2015 post warns that these reader optimizations may have little effect.
The accounts were published by Facebook in 2013, 2014, and 2015, and their performance figures describe Facebook’s systems and test setups at those times. They explain how the company pursued lower latency and less wasted work; they do not establish a current, transferable speed ratio for a different warehouse.
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