March 17, 2020
Today I am announcing that my last day at MongoDB will be July 10th.
It’s been an incredible 12.5 years at MongoDB that, in a way, all goes back to my days as an undergraduate CS student in the early 2000s. While many of the topics in Brown University’s database systems class were applicable to any database, like b-trees and -- of course -- pointer swizzling, everything was ultimately presented as a building block for relational databases. Any other database style was relegated to the final, semester-ending lecture.
It made sense at the time to teach things that way. But in every software endeavor I worked on, from trivial weekend projects to multi-year enterprises, databases just kept getting in the way. Often maddeningly so.
After many years of partnering on multiple applications, Dwight Merriman and I set out to make a database we actually wanted to use, so we would never again have to deal with the tyranny of relational databases. We wanted something that was, at its core, born of the same assumptions that we, as developers, have about programming. Data should be stored the way you think about working with it. Databases should be able to scale horizontally and be spread globally like any other parts of your applications. Change is inevitable and iteration is more important than knowing answers ahead of time.
I’m proud to say we’ve absolutely accomplished what Dwight and I set out to do. Not only is MongoDB the database I’d always wished I had, but the database landscape itself is entirely transformed. No self-respecting database would be caught without a document solution, and distributed systems are becoming the new norm. Moreover, by pursuing new ideas, we helped spur more innovation in the database space in the past ten years than had been seen in decades. I predict that in another ten years, the document model and distributed databases will be the standard. Now I can look forward to tackling many new challenges, knowing that I won’t have to worry about the database getting in my way.
An outcome I’m equally proud of is that in the pursuit of that technical goal, we built a phenomenal engineering team with a strong commitment to collaboration, honesty, ambition, and ownership. The early days of me and Dwight moving fast and experimenting were great fun, but without this team’s incredible efforts, MongoDB would have remained a pipe dream, and I doubt we’d have seen the explosion of use cases in the market that we now take for granted.
I will miss building MongoDB, but I leave it confidently in the team’s brilliant, capable, and talented hands. I look forward to seeing what they will accomplish.
Getting Storage Engines Ready for Fast Storage Devices
Over the past two decades, performance of storage hardware increased by two orders of magnitude. First, with the introduction of solid state drives (SSD), then with the transition from SATA to PCIe, and finally with the innovation in non-volatile memory technology and the manufacturing process [ 1 , 7 ]. More recently, in April 2019, Intel released the first commercial Storage Class Memory (SCM). Its Optane DC Persistent Memory, built with 3D XPoint technology, sits on a memory bus and further reduces I/O latency [ 2 ]. While device access used to dominate I/O latency, the cost of navigating the software stack of a storage system is becoming more prominent as devices’ access time shrinks. This is resulting in a flurry of academic research and in changes to commercially used operating systems (OS) and file systems. Despite these efforts, mainstream system software is failing to keep up with rapidly evolving hardware. Studies [ 4 , 5 , 6 ] have shown that file system and other OS overhead still dominates the cost of I/O in very fast storage devices, such as SCMs. In response to these challenges, academics proposed a new user-level file system, SplitFS [ 6 ], that substantially reduces these overheads. Unfortunately, adopting a user-level file system is not a viable option for many commercial products. Apart from concerns about correctness, stability, and maintenance, adoption of SplitFS would restrict portability, as it only runs on Linux and only on top of the ext4-DAX file system. Fortunately, there IS something that can be done in software storage engines that care about I/O performance. Within MongoDB’s storage engine, WiredTiger, we were able to essentially remove the brakes that the file system applied to our performance without sacrificing the convenience it provides or losing portability. Our changes rely on using memory-mapped files for I/O and batching expensive file system operations. These changes resulted in up to 63% performance improvements for 19 out of 65 benchmarks on mainstream SSDs. Streamlining I/O in WiredTiger Our changes to WiredTiger were inspired by a study from UCSD [ 4 ], where the authors demonstrated that by using memory-mapped files for I/O and by pre-allocating some extra space in the file whenever it needed to grow, they could achieve almost the same performance as if the file system was completely absent. Memory-mapped files Memory-mapped files work as follows. The application makes an mmap system call, whereby it requests the operating system to “map” a chunk of its virtual address space to a same-sized chunk in the file of its choice (Step 1 in Fig.1). When it accesses memory in that portion of the virtual address space for the first time (e.g., virtual page 0xABCD in Fig. 1), the following events take place: Since this is a virtual address that has not been accessed before, the hardware will generate a trap and transfer control to the operating system. The operating system will determine that this is a valid virtual address, ask the file system to read the corresponding page-sized part of the file into its buffer cache, and Create a page table entry mapping the user virtual page to the physical page in the buffer cache (e.g., physical page 0xFEDC in Fig.1), where that part of the file resides (Step 2 in Fig 1). Finally, the virtual-to-physical translation will be inserted into the Translation Lookaside Buffer (TLB -- a hardware cache for these translations), and the application will proceed with the data access. Memory mapped files work as follows: (1) They establish a virtual memory area for the mapped file, (2) They place the virtual-to-physical address translation into the page table, (3) They cache the translation in the Translation Lookaside Buffer (TLB) Subsequent accesses to the same virtual page may or may not require operating system involvement, depending on the following: If the physical page containing the file data is still in the buffer cache and the page table entry is in the TLB, operating system involvement is NOT necessary, and the data will be accessed using regular load or store instructions. If the page containing the file data is still in the buffer cache, but the TLB entry was evicted, the hardware will transition into kernel mode, walk the page table to find the entry (assuming x86 architecture), install it into the TLB and then let the software access the data using regular load or store instructions. If the page containing the file data is not in the buffer cache, the hardware will trap into the OS, which will ask the file system to fetch the page, set up the page table entry, and proceed as in scenario 2. In contrast, system calls cross the user/kernel boundary every time we need to access a file. Even though memory-mapped I/O also crosses the user/kernel boundary in the second and third scenarios described above, the path it takes through the system stack is more efficient than that taken by system calls. Dispatching and returning from a system call adds CPU overhead that memory-mapped I/O does not have [ 8 ]. Furthermore, if the data is copied from the memory mapped file area to another application buffer, it would typically use a highly optimized AVX-based implementation of memcpy. When the data is copied from the kernel space into the user space via a system call, the kernel has to use a less efficient implementation, because the kernel does not use AVX registers [ 8 ]. Pre-allocating file space Memory-mapped files allow us to substantially reduce the involvement of the OS and the file system when accessing a fixed-sized file. If the file grows, however, we do need to involve the file system. The file system will update the file metadata to indicate its new size and ensure that these updates survive crashes. Ensuring crash consistency is especially expensive, because each journal record must be persisted to storage to make sure it is not lost in the event of a crash. If we grow a file piecemeal, we incur that overhead quite often. That is why the authors of SplitFS [ 6 ] and the authors of the UCSD study [ 4 ] both pre-allocate a large chunk of the file when an application extends it. In essence, this strategy batches file system operations to reduce their overhead. Our Implementation The team applied these ideas to WiredTiger in two phases. First, we implemented the design where the size of the mapped file area never changes. Then, after making sure that this simple design works and yields performance improvements, we added the feature of remapping files as they grow or shrink. That feature required efficient inter-thread synchronization and was the trickiest part of the whole design -- we highlight it later in this section. Our changes have been in testing in the develop branch of WiredTiger as of January 2020. As of the time of the writing, these changes are only for POSIX systems; a Windows port is planned for the future. Assuming a fixed-size mapped file area Implementing this part required few code changes. WiredTiger provides wrappers for all file-related operations, so we only needed to modify those wrappers. Upon opening the file, we issue the mmap system call to also map it into the virtual address space. Subsequent calls to wrappers that read or write the file will copy the desired part of the file from the mapped area into the supplied buffer. WiredTiger allows three ways to grow or shrink the size of the file. The file can grow explicitly via a fallocate system call (or its equivalent), it can grow implicitly if the engine writes to the file beyond its boundary, or the file can shrink via the truncate system call. In our preliminary design we disallowed explicitly growing or shrinking the file, which did not affect the correctness of the engine. If the engine writes to the file beyond the mapped area, our wrapper functions simply default to using system calls. If the engine then reads the part of the file that had not been mapped, we also resort to using a system call. While this implementation was decent as an early prototype, it was too limiting for a production system. Resizing the mapped file area The trickiest part of this feature is synchronization. Imagine the following scenario involving two threads, one of which is reading the file and another one truncating it. Prior to reading, the first thread would do the checks on the mapped buffer to ensure that the offset from which it reads is within the mapped buffer’s boundaries. Assuming that it is, it would proceed to copy the data from the mapped buffer. However, if the second thread intervenes just before the copy and truncates the file so that its new size is smaller than the offset from which the first thread reads, the first thread’s attempt to copy the data would result in a crash. This is because the mapped buffer is larger than the file after truncation and attempting to copy data from the part of the buffer that extends beyond the end of the file would generate a segmentation fault. An obvious way to prevent this problem is to acquire a lock every time we need to access the file or change its size. Unfortunately, this would serialize I/O and could severely limit performance. Instead, we use a lock-free synchronization protocol inspired by read-copy-update (RCU) [ 9 ]. We will refer to all threads that might change the size of the file as writers. A writer, therefore, is any thread that writes beyond the end of the file, extends it via a fallocate system call, or truncates it. A reader is any thread that reads the file. Our solution works as follows: A writer first performs the operation that changes the size of the file and then remaps the file into the virtual address space. During this time we want nobody else accessing the mapped buffer, neither readers nor writers. However, it is not necessary to prevent all I/O from occurring at this time; we can simply route I/O to system calls while the writer is manipulating the mapped buffer, since system calls are properly synchronized in the kernel with other file operations. To achieve these goals without locking, we rely on two variables: mmap_resizing: when a writer wants to indicate to others that it is about to exclusively manipulate the mapped buffer, it atomically sets this flag. mmap_use_count: a reader increments this counter prior to using the mapped buffer, and decrements it when it is done. So this counter tells us if anyone is currently using the buffer. The writer waits until this counter goes to zero before proceeding. Before resizing the file and the mapped buffer, writers execute the function prepare_remap_resize_file ; its pseudocode is shown below. Essentially, the writer efficiently waits until no one else is resizing the buffer, then sets the resizing flag to claim exclusive rights to the operation. Then, it waits until all the readers are done using the buffer. prepare_remap_resize_file: wait: /* wait until no one else is resizing the file */ while (mmap_resizing != 0) spin_backoff(...); /* Atomically set the resizing flag, if this fails retry. */ result = cas(mmap_resizing, 1, …); if (result) goto wait; /* Now that we set the resizing flag, wait for all readers to finish using the buffer */ while (mmap_use_count > 0) spin_backoff(...); After executing prepare_remap_resize_file , the writer performs the file-resizing operation, unmaps the buffer, remaps it with the new size and resets the resizing flag. The synchronization performed by the readers is shown in the pseudocode of the function read_mmap : read_mmap: /* Atomically increment the reference counter, * so no one unmaps the buffer while we use it. */ atomic_add(mmap_use_count, 1); /* If the buffer is being resized, use the system call instead of the mapped buffer. */ if (mmap_resizing) atomic_decr(mmap_use_count, 1); read_syscall(...); else memcpy(dst_buffer, mapped_buffer, …); atomic_decr(mmap_use_count, 1); As a side note, threads writing the file must perform both the reader synchronization, as in read_mmap, to see if they can use the memory-mapped buffer for I/O, and the writer synchronization in the case they are writing past the end of the file (hence extending its size). Please refer to the WiredTiger develop branch for the complete source code. Batching file system operations As we mentioned earlier, a crucial finding of the UCSD study that inspired our design [ 4 ], was the need to batch expensive file system operations by pre-allocating file space in large chunks. Our experiments with WiredTiger showed that it already uses this strategy to some extent. We ran experiments comparing two configurations: (1) In the default configuration WiredTiger uses the fallocate system call to grow files. (2) In the restricted configuration WiredTiger is not allowed to use fallocate and thus resorts to implicitly growing files by writing past their end. We measured the number of file system invocations in both cases and found that it was at least an order of magnitude smaller in the default configuration than in the restricted. This tells us that WiredTiger already batches file system operations. Investigating whether batching can be optimized for further performance gains is planned for the future. Performance To measure the impact of our changes, we compared the performance of the mmap branch and the develop branch on the WiredTiger benchmark suite WTPERF. WTPERF is a configurable benchmarking tool that can emulate various data layouts, schemas, and access patterns while supporting all kinds of database configurations. Out of 65 workloads, the mmap branch improved performance for 19. Performance of the remaining workloads either remained unchanged or showed insignificant changes (within two standard deviations of the average). Variance in performance of two workloads (those that update a log-structured merge tree) increased by a few percent, but apart from these, we did not observe any downsides to using mmap. The figures below show the performance improvement, in percent, of the mmap branch relative to develop for the 19 benchmarks where mmap made a difference. The experiments were run on a system with an Intel Xeon processor E5-2620 v4 (eight cores), 64GB of RAM and an Intel Pro 6000p series 512GB SSD drive. We used default settings for all the benchmarks and ran each at least three times to ensure the results are statistically significant. All but 2 of the benchmarks where mmap made a difference show significant improvements Overall, there are substantial performance improvements for these workloads, but there are a couple interesting exceptions. For 500m-btree-50r50u and for update-btree some operations (e.g., updates or inserts) are a bit slower with mmap, but others (typically reads) are substantially faster. It appears that some operations benefit from mmap at the expense of others; we are still investigating why this is happening. One of the variables that explains improved performance with mmap is increased rate of I/O. For example, for the 500m-btree-50r50u workload (this workload simulates a typical MongoDB load) the read I/O rate is about 30% higher with mmap than with system calls. This statistic does not explain everything: after all, read throughput for this workload is 63% better with mmap than with system calls. Most likely, the rest of the difference is due to more efficient code paths of memory-mapped I/O (as opposed to going through system calls), as observed in earlier work . Indeed, we typically observe a higher CPU utilization when using mmap. Conclusion Throughput and latency of storage devices improve at a higher rate than CPU speed thanks to radical innovations in storage technology and the placement of devices in the system. Faster storage devices reveal inefficiencies in the software stack. In our work we focussed on overhead related to system calls and file system access and showed how it can be navigated by employing memory-mapped I/O. Our changes in the WiredTiger storage engine yielded up to 63% improvement in read throughput. For more information on our implementation, we encourage you to take a look at the files os_fs.c and os_fallocate.c in the os_posix directory of the WiredTiger develop branch . References  List of Intel SSDs. https://en.wikipedia.org/wiki/List_of_Intel_SSDs  Optane DC Persistent Memory. https://www.intel.ca/content/www/ca/en/architecture-and-technology/optane-dc-persistent-memory.html  Linux® Storage System Analysis for e.MMC with Command Queuing, https://www.micron.com/-/media/client/global/documents/products/white-paper/linux_storage_system_analysis_emmc_command_queuing.pdf?la=en  Jian Xu, Juno Kim, Amirsaman Memaripour, and Steven Swanson. 2019. Finding and Fixing Performance Pathologies in Persistent Memory Software Stacks. In 2019 Architectural Support for Program- ming Languages and Operating Systems (ASPLOS ’19). http://cseweb.ucsd.edu/~juk146/papers/ASPLOS2019-APP.pdf  Jian Xu and Steven Swanson, NOVA: A Log-structured File System for Hybrid Volatile/Non-volatile Main Memories, 14th USENIX Conference on File and Storage Technologies (FAST’16). https://www.usenix.org/system/files/conference/fast16/fast16-papers-xu.pdf  Rohan Kadekodi, Se Kwon Lee, Sanidhya Kashyap, Taesoo Kim, Aasheesh Kolli, and Vijay Chidambaram. 2019. SplitFS: reducing software overhead in file systems for persistent memory. In Proceedings of the 27th ACM Symposium on Operating Systems Principles (SOSP ’19). https://www.cs.utexas.edu/~vijay/papers/sosp19-splitfs.pdf  SDD vs HDD. https://www.enterprisestorageforum.com/storage-hardware/ssd-vs-hdd.html  Why mmap is faster than system calls. https://medium.com/@sasha_f/why-mmap-is-faster-than-system-calls-24718e75ab37  Paul McKinney. What is RCU, fundamentally? https://lwn.net/Articles/262464/ If you found this interesting, be sure to tweet it . Also, don't forget to follow us for regular updates.
Honoring Latine Heritage Month at MongoDB
Heritage and culture sits at the centerfold of human interaction. With a population of more than 650 million people, speaking over 400 different languages, and spanning a geographic area from the tip of Patagonia to the Caribbean, the people of Latin America and the culture of their 33 countries are difficult to condense into one identity. In celebration of Latine Heritage Month, we asked a few Latine MongoDB employees to reflect on their heritage and ultimately how that shapes their work. Tayrin S Riojas , Head of Government Relations and Public Policy I was born in Los Angeles and moved to Mexico City before my third birthday. In my junior year of high school, my family moved back to the United States and ended up in Dallas. I feel so incredibly fortunate to have experienced living in both countries for extended periods of time. I remember high school in the United States feeling like I was in a Hollywood movie — there were big lockers, cheerleaders, and sports teams. However, I felt my friends in Mexico City had a wider variety of social activities compared to the friends I made in the United States. As Mexicans, and in many Latino cultures, we are passionate and socially driven with our families, extended families, and friendships. This is what I personally love most about my culture. We have great traditions and share in them together, from posadas, piñatas, soccer games, and even mourning. This is something that transcends our location, and I feel honored to have been raised with these values. Throughout my career, I have worked in telecommunications, film post-production, healthcare, and the government and held roles such as lobbyist, Senate Committee Consultant, and International Relations Advisor. Tech is at the core of every single one of these opportunities. I am certainly not an engineer, nor can I code anything functional, but I do have a passion for learning about technology. After having my second “COVID baby” and being on parental leave, I decided I wanted to get back into tech. A relative recommended MongoDB, and soon after, I started as a Cloud Account Executive for the Latin America market. I loved talking to our customers, and it taught me so much about the power and versatility of our tech. It was a great role, but I had spent so much time working with the government that I honestly missed it. I truly believe that to excel at what you do, you must have your heart in it. MongoDB is growing fast, and we are encouraged to build our own careers here. When I realized we had no Government Affairs department, I decided to propose it. I wrote a paper on why Government Affairs, why now, and the incredible value and ROI this could have for us (especially with our partnerships). I sent my proposal to leadership for their consideration. From ideation to leadership approving the department and role, I had amazing mentors, guidance, and support from other women at MongoDB and employee resource groups like Sell Like a Girl and The Underrepresented People of Color. I am now the Head of Government Relations and Public Policy at MongoDB. As a Latina woman, having a company of MongoDB’s size make room for your ideas and contributions has been an incredibly fulfilling journey. There is still much work to be done to build our Government Affairs department, but I am incredibly blessed to work for people I admire and contribute to the company through a role I am passionate about. If you are looking for a great career in tech, I urge you to consider MongoDB. Adriano Fratelli , Customer Success Manager My family’s history in Brazil began with my grandparents who migrated from Calabria, Italy to São Paulo in the mid-1960s. My grandfather had received a job opportunity in the largest and most modern port in Latin America, Santos. Growing up in São Paulo, my childhood was rich with Brazilian culture. I was surrounded by family, music, dancing, great food, festivals (like Brazilian Carnival ), and sports. My journey into technology began with my father. He worked for 40 years as a technology product manager in the retail industry and inspired me to pursue a career in tech. I finished my degree in Information Systems in 2014 and started my professional career at IBM as a Field Technical Sales Specialist. I then worked at Lenovo and Oracle before looking for a new career opportunity. My decision to start a new journey at MongoDB was due to the great perspective that customers have regarding our products and services, along with MongoDB’s inclusive culture. The world of technology has opened up many opportunities in my personal life by helping me improve my English language skills and giving me exposure to different countries and cultures around the world. MongoDB is growing exponentially in the Latin American region and, as part of the Customer Success team, I enjoy that I’m able to help our customers onboard and adopt MongoDB’s services. One thing that makes working at MongoDB stand out is knowing that employee’s differences are embraced and our ideas are heard. As part of a global team, it’s great to know that I have the space and support to share my ideas and am valued for the unique perspective I bring. Read more stories from Hispanic and Latine employees at MongoDB . We’re embracing differences every day at MongoDB. Join us to make an impact and transform your career.